
# Import All required libaries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import seaborn as sns
import statsmodels.api as sm

from bs4 import BeautifulSoup
import requests
from time import sleep


import sklearn
from sklearn import neighbors, tree, ensemble, naive_bayes, svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LogisticRegression
from sklearn import linear_model

from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV

from sklearn.neighbors import KNeighborsClassifier
# *** Decision Tree; Random Forest
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
# *** Naive Bayes
from sklearn.naive_bayes import GaussianNB
# *** SVM classifier
from sklearn.svm import SVC
# --------  metrics:
from sklearn import metrics
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
from sklearn.metrics import make_scorer


#Players Stats URLs Seasson 2021-2022
url_players_stats = "https://fbref.com/en/comps/Big5/stats/players/Big-5-European-Leagues-Stats"
url_player_passing = "https://fbref.com/en/comps/Big5/passing/players/Big-5-European-Leagues-Stats"
url_players_shooting = "https://fbref.com/en/comps/Big5/shooting/players/Big-5-European-Leagues-Stats"
url_player_creation = "https://fbref.com/en/comps/Big5/gca/players/Big-5-European-Leagues-Stats"
url_player_defense = "https://fbref.com/en/comps/Big5/defense/players/Big-5-European-Leagues-Stats"
url_players_miscellaneous  = "https://fbref.com/en/comps/Big5/misc/players/Big-5-European-Leagues-Stats"
url_list=[url_players_stats, url_player_passing, url_players_shooting, url_player_creation, url_player_defense, url_players_miscellaneous]


# Function to Check url status
def check_url_status(url):
    response_stats = requests.get(url)
    if((response_stats.status_code) == 200):
        status = "OK"
    else:
        status = "ERROR"
    return status


# Check all URls Response code

for i in range(len(url_list)):
    url_status = check_url_status(url_list[i])
    print("status:",url_status, "-" ,url_list[i])

status: OK - https://fbref.com/en/comps/Big5/stats/players/Big-5-European-Leagues-Stats
status: OK - https://fbref.com/en/comps/Big5/passing/players/Big-5-European-Leagues-Stats
status: OK - https://fbref.com/en/comps/Big5/shooting/players/Big-5-European-Leagues-Stats
status: OK - https://fbref.com/en/comps/Big5/gca/players/Big-5-European-Leagues-Stats
status: OK - https://fbref.com/en/comps/Big5/defense/players/Big-5-European-Leagues-Stats
status: OK - https://fbref.com/en/comps/Big5/misc/players/Big-5-European-Leagues-Stats


# Create Soup and table for all URLs
soup_stats = []
soup_pass = []
soup_shoot =[]
soup_creation = []
soup_defense = []
soup_miscellaneous = []

table_stats = []
table_pass = []
table_shoot = []
table_creation = []
table_defense = []
table_miscellaneous = []

soup_list = [soup_stats, soup_pass, soup_shoot, soup_creation, soup_defense, soup_miscellaneous]
table_list = [table_stats, table_pass, table_shoot, table_creation, table_defense, table_miscellaneous]

for i in range(len(soup_list)):
    response_stats = requests.get(url_list[i])
    print(url_list[i])
    soup_list[i] = BeautifulSoup(response_stats.text,"html.parser")
    if(soup_list[i] == None):
        print("Unable to Create soup")
    else:
        print("Soup Created")
        table_list[i] = soup_list[i].find("table")

        
        
soup_stats = soup_list[0]
soup_pass = soup_list[1]
soup_shoot =soup_list[2]
soup_creation = soup_list[3]
soup_defense = soup_list[4]
soup_miscellaneous = soup_list[5]

table_stats = table_list[0]
table_pass = table_list[1]
table_shoot = table_list[2]
table_creation = table_list[3]
table_defense = table_list[4]
table_miscellaneous = table_list[5]

https://fbref.com/en/comps/Big5/stats/players/Big-5-European-Leagues-Stats
Soup Created
https://fbref.com/en/comps/Big5/passing/players/Big-5-European-Leagues-Stats
Soup Created
https://fbref.com/en/comps/Big5/shooting/players/Big-5-European-Leagues-Stats
Soup Created
https://fbref.com/en/comps/Big5/gca/players/Big-5-European-Leagues-Stats
Soup Created
https://fbref.com/en/comps/Big5/defense/players/Big-5-European-Leagues-Stats
Soup Created
https://fbref.com/en/comps/Big5/misc/players/Big-5-European-Leagues-Stats
Soup Created


# Create DataFrame for Players Main Stats (1/6)

# table colums from inspect url
headers_stats = ['player_name','nationality','position','team','league','age','birth_year','match_played','match_starts','minutes_played','minutes_played_by_90m','goals','assists','goals_no_pen','goals_pen','pens_att','yellow_card','red_cards','goals_per_90','assists_per_90','goals_and_assists_per_90','goals_no_pen_per_90','goals_no_pen_and_assists_per_90','expected_goals','expected_goals_no_pen','expected_assists','expected_goals_and_assists_no_pen','expected_goals_per_90','expected_assists_per_90','expected_goals_and_assists_per_90','expected_goals_no_pen_per_90','expected_goals_and_assists_no_pen_per_90','matches']
df_stats = pd.DataFrame(columns=headers_stats)

i=0
for j in table_stats.find_all('tr')[1:]:
    row_data = j.find_all('td')
    row = [tr.text for tr in row_data]
    if row != []:
        #print(row)
        df_stats.loc[i] = row
        i=i+1
        
df_stats


# Create DataFrame for Players Passing Stats (2/6)

# table colums from inspect url
headers_passing = ['player_name','nationality','position','team','league','age','birth_year','minutes_played_by_90m','total_pass_completed','total_pass_attempted','pass_completed_percentage','total_pass_dist','total_pass_dist_forward','short_pass_completed','short_pass_attempted','short_pass_percentage','mid_pass_completed','mid_pass_attempted','mid_pass_percentage','long_pass_completed','long_pass_attempted','long_pass_percentage','assists','expected_assists','assists_minus_expected_assists','pass_lead_to_shot','pass_30_court(na)','pass_18_court(na)','crosses_18_court(na)','progressive_pass(na)','matches']
df_passing = pd.DataFrame(columns=headers_passing)

i=0
for j in table_pass.find_all('tr')[1:]:
    row_data = j.find_all('td')
    row = [tr.text for tr in row_data]
    if row != []:
        #print(row)
        df_passing.loc[i] = row
        i=i+1
        
df_passing


# Create DataFrame for Players Shooting Stats (3/6)

# table colums from inspect url
headers_shooting = ['player_name','nationality','position','team','league','age','birth_year','match_by_90m','goals','total_shots','shots_on_target','shots_on_target_percent','total_shots_per_90m','shots_on_target_per_90m','goals_per_shots','goals_per_shot_on_target','average_shot_distance_meter','free_kick_shots','pens_made','pens_attempted','expected_goals','expected_goals_no_pens','expected_goal_per_shot_no_pens','goals_minus_expected_goals','non_pens_goals_minus_expected_pens_goals','matches']
df_shooting = pd.DataFrame(columns=headers_shooting)

i=0
for j in table_shoot.find_all('tr')[1:]:
    row_data = j.find_all('td')
    row = [tr.text for tr in row_data]
    if row != []:
        #print(row)
        df_shooting.loc[i] = row
        i=i+1
        
df_shooting


# Create DataFrame for Players Goals Creation Stats (4/6)

# table colums from inspect url
headers_creation = ['player_name','nationality','position','team','league','age','birth_year','minutes_played_by_90m','total_shots_creation','shot_creation_by_90m','total_success_pass','total_miss_pass','total_success_dribble','total_shots_lead_to_attempt','total_earned_fouls','defense_action_lead_to_attempt','total_goal_creation','goal_creation_by_90m','total_pass_lead_to_goal','total_miss_pass_lead_to_goal','total_dribble_lead_to_goal','total_shots_lead_to_goal','total_earned_fould_lead_to_goal','defense_action_lead_to_goal','matches']
df_creation = pd.DataFrame(columns=headers_creation)

i=0
for j in table_creation.find_all('tr')[1:]:
    row_data = j.find_all('td')
    row = [tr.text for tr in row_data]
    if row != []:
        #print(row)
        df_creation.loc[i] = row
        i=i+1
        
df_creation


# Create DataFrame for Players Defensive Stats (5/6)

# table colums from inspect url
headers_defense = ['player_name','nationality','position','team','league','age','birth_year','minutes_played_by_90m','total_tackels','total_tackels_won','total_defensive_tackels','total_mid_tackels','total_attack_tackels','total_dribblers_tackled','total_dribbles_contested','dribbles_tackeld_by_percentage','total_dribbled_past','total_pressures','total_successful_pressures','successful_pressure_percentage','total_pressure_defense','total_pressure_mid','total_pressure_attack','total_blocks','total_shots_blocked','total_shots_saved','total_passes_blocked','total_interceptions','total_tackels_plus_interceptions','total_clearances','total_mistakes','matches']
df_defense = pd.DataFrame(columns=headers_defense)

i=0
for j in table_defense.find_all('tr')[1:]:
    row_data = j.find_all('td')
    row = [tr.text for tr in row_data]
    if row != []:
        #print(row)
        df_defense.loc[i] = row
        i=i+1
        
df_defense


# Create DataFrame for Players Miscellaneous Stats (6/6)

# table colums from inspect url
headers_miscellaneous = ['player_name','nationality','position','team','league','age','birth_year','minutes_by_90','yellow_cards','red_cards','second_yellow_card','fouls_commited','fouls_drawn','offsides','crosses','interceptions','tackels_won','pens_won','pens_conceded','own_goals','ball_recoveries','aerials_won','aerials_lost','aerials_won_percent','matches']
df_miscellaneous = pd.DataFrame(columns=headers_miscellaneous)

i=0
for j in table_miscellaneous.find_all('tr')[1:]:
    row_data = j.find_all('td')
    row = [tr.text for tr in row_data]
    if row != []:
        #print(row)
        df_miscellaneous.loc[i] = row
        i=i+1
        
df_miscellaneous


# Function to load CSV with specific encoding for utf 8
def load_csv(path):
    df = pd.read_csv(path,encoding='utf-8')
    return df


# Function to Export csv function with utf 8 encoding
def export_csv(df,path,filename):
    filename = "{}.csv".format(filename)
    path = "{}\{}".format(path,filename)
    df.to_csv(path,encoding='utf-8')


# Test Function load_csv
#df_stats = load_csv("C:\Python_Projects\CSV\player_stats_dataframe.csv")


# Create New DataFrame with by custom columns
custom_headers = ["first_name","last_name","full_name","team","position","value"]
df_values = pd.DataFrame(columns=custom_headers)


# Move Columns from df_stats to df_values
df_values["full_name"] = df_stats["player_name"].values
df_values["team"] = df_stats["team"].values
df_values["position"] = df_stats["position"].values


# Function to Split player full_name to first_name and last_name
# Needed for searchine player value in transfermarket website later
def split_first_last(full_name):
    if " " not in full_name:
        first_name = full_name
        last_name = "NaN"
        return first_name,last_name
    else:
        first_name = full_name.split(" ")[0]
        last_name = full_name.split(" ",1)[1]
        return first_name,last_name


# Testing Split function
full_name = "Miguel Ángel Rubio"
first_name, last_name = split_first_last(full_name)
print("first name:",first_name,"\nlast name:",last_name)

first name: Miguel 
last name: Ángel Rubio


# Fill Columns Data in df_values with full_name, first_name, last_name

i=0

for full_name in df_values["full_name"]:
    first_name, last_name = split_first_last(full_name)
    df_values["first_name"][i] = first_name
    df_values["last_name"][i] = last_name
    i=i+1


df_values


# Function to check player url status

def check_player_url(url):
    headers = {'User-Agent': 
           'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}
    response_stats = requests.get(url, headers=headers)
    if((response_stats.status_code) == 200):
        status = "OK"
    else:
        status = "ERROR"  
    return status


# Function to check if player page exist or not in transfermarket
def search_player(transfermarket_soup):
    #check if header h2 exist
    value_h2 = transfermarket_soup.find_all('h2')
    if(value_h2):
        status = "NOT_FOUND"
    else:
        status = "FOUND"
    return status


# Function to get player value from transfermarket url
# return value if player exist and found, return NaN other.

def get_player_value(first_name,last_name,team):
    # get first char
    first_name = first_name[0]
    
    transfermarket_base_url = "https://www.transfermarkt.us/schnellsuche/ergebnis/schnellsuche?query={}%2C{}%3A{}".format(last_name,first_name,team)
    # check url
    status = check_player_url(transfermarket_base_url)
    if(status == 'OK'):
        headers = {'User-Agent': 
           'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}
        transfermarket_page_details = requests.get(transfermarket_base_url, headers=headers)
        transfermarket_soup = BeautifulSoup(transfermarket_page_details.text, 'html.parser')
        search_status = search_player(transfermarket_soup)
        
        if(search_status == "FOUND"):
            value = transfermarket_soup.find_all("td",{"class":"rechts hauptlink"})
            # retrun player value
            return value[0].text
        else:
            value = "NaN"
            return value
    else:
        value = "NaN"
        return value


# Search for each player value and fill the value columns in df_values
i=0
length = len(df_values["value"])

for value in range(len(df_values["value"])):
    first_name = df_values["first_name"][i]
    last_name = df_values["last_name"][i]
    team = df_values["team"][i]
    
    value = get_player_value(first_name,last_name,team)
    df_values["value"][i] = value
    print(i,"/",length, df_values["full_name"][i], "=",value)
    i=i+1

0 / 2921 Max Aarons = $24.20m
1 / 2921 Yunis Abdelhamid = $1.32m
2 / 2921 Salis Abdul Samed = $3.30m
3 / 2921 Laurent Abergel = $3.30m
4 / 2921 Charles Abi = NaN
5 / 2921 Dickson Abiama = $880Th.
6 / 2921 Matthis Abline = NaN
7 / 2921 Tammy Abraham = $55.00m
8 / 2921 Luis Abram = NaN
9 / 2921 Francesco Acerbi = $4.40m
10 / 2921 Ragnar Ache = NaN
11 / 2921 Mohamed Achi = $330Th.
12 / 2921 Marcos Acuña = $19.80m
13 / 2921 Che Adams = $19.80m
14 / 2921 Tyler Adams = $18.70m
15 / 2921 Sargis Adamyan = NaN
16 / 2921 Martin Adeline = $880Th.
17 / 2921 Amine Adli = $13.20m
18 / 2921 Yacine Adli = $9.90m
19 / 2921 Michel Aebischer = $3.85m
20 / 2921 Felix Afena-Gyan = $6.60m
21 / 2921 Martin Agirregabiria = NaN
22 / 2921 Julen Agirrezabala = NaN
23 / 2921 Lucien Agoume = $7.70m
24 / 2921 Kevin Agudelo = $3.85m
25 / 2921 Nayef Aguerd = $13.20m
26 / 2921 Sergio Agüero = NaN
27 / 2921 Ruben Aguilar = $11.00m
28 / 2921 Álvaro Aguirre = -
29 / 2921 Naouirou Ahamada = $1.65m
30 / 2921 Anel Ahmedhodzic = $6.60m
31 / 2921 Jean-Eudes Aholou = $3.30m
32 / 2921 Joseph Aidoo = $7.70m
33 / 2921 Ola Aina = $3.85m
34 / 2921 Rayan Aït Nouri = NaN
35 / 2921 Kristoffer Ajer = $19.80m
36 / 2921 Ludovic Ajorque = $17.60m
37 / 2921 Manuel Akanji = $33.00m
38 / 2921 Carlos Akapo = $1.32m
39 / 2921 Marley Aké = $2.75m
40 / 2921 Nathan Aké = $27.50m
41 / 2921 Ilias Akhomach = $3.30m
42 / 2921 Maghnes Akliouche = $1.10m
43 / 2921 Paul Akouokou = $4.40m
44 / 2921 Jean-Daniel Akpa-Akpro = NaN
45 / 2921 Kevin Akpoguma = $6.60m
46 / 2921 David Alaba = $60.50m
47 / 2921 Sofiane Alakouch = NaN
48 / 2921 Tomás Alarcón = $3.30m
49 / 2921 Lucas Alario = $8.80m
50 / 2921 Jordi Alba = $9.90m
51 / 2921 Josué Albert = $330Th.
52 / 2921 Luis Alberto = $30.80m
53 / 2921 Raúl Albiol = $3.30m
54 / 2921 Marc Albrighton = $2.75m
55 / 2921 Paco Alcácer = $9.90m
56 / 2921 Thiago Alcántara = $22.00m
57 / 2921 Rubén Alcaraz = $2.75m
58 / 2921 Omar Alderete = $11.00m
59 / 2921 Iván Alejo = $1.32m
60 / 2921 Doğan Alemdar = $3.85m
61 / 2921 Carles Aleñá = $9.90m
62 / 2921 Trent Alexander-Arnold = $88.00m
63 / 2921 Mircea Alexandru Tirlea = NaN
64 / 2921 Mohamed Ali Cho = NaN
65 / 2921 Alisson = NaN
66 / 2921 Allan = NaN
67 / 2921 Jim Allevinah = $2.20m
68 / 2921 Dele Alli = NaN
69 / 2921 Dele Alli = $17.60m
70 / 2921 Miguel Almirón = NaN
71 / 2921 Marcos Alonso = $13.20m
72 / 2921 Giorgio Altare = $2.20m
73 / 2921 Hugo Álvarez = -
74 / 2921 Yeray Álvarez = NaN
75 / 2921 Dani Alves = $1.10m
76 / 2921 Steven Alzate = $5.50m
77 / 2921 Ibrahim Amadou = $2.20m
78 / 2921 Emmanuel Amankwaa Akurugu = NaN
79 / 2921 Daniel Amartey = $11.00m
80 / 2921 Amath = NaN
81 / 2921 Jordan Amavi = $6.60m
82 / 2921 Jordan Amavi = NaN
83 / 2921 Wisdom Amey = -
84 / 2921 Kelvin Amian = $3.85m
85 / 2921 Nadiem Amiri = NaN
86 / 2921 Nadiem Amiri = $8.80m
87 / 2921 Ethan Ampadu = $14.30m
88 / 2921 Sofyan Amrabat = $11.00m
89 / 2921 Joachim Andersen = $27.50m
90 / 2921 Felipe Anderson = $14.30m
91 / 2921 Sebastian Andersson = $2.20m
92 / 2921 Florin Andone = $1.10m
93 / 2921 Andrés Andrade Cedeño = NaN
94 / 2921 Fredrik André Bjørkan = NaN
95 / 2921 Benjamin André = $13.20m
96 / 2921 Robert Andrich = $14.30m
97 / 2921 José Ángel = NaN
98 / 2921 Angeliño = NaN
99 / 2921 Ebenezer Annan = $83Th.
100 / 2921 Cristian Ansaldi = $770Th.
101 / 2921 Janis Antiste = $4.40m
102 / 2921 Waldemar Anton = $6.60m
103 / 2921 Josep Antoni Gayá = NaN
104 / 2921 Michail Antonio = $11.00m
105 / 2921 Christopher Antwi-Adjei = $1.10m
106 / 2921 Houssem Aouar = $27.50m
107 / 2921 Adil Aouchiche = $6.60m
108 / 2921 Dennis Appiah = $2.20m
109 / 2921 Mauro Arambarri = $16.50m
110 / 2921 Mattia Aramu = $4.40m
111 / 2921 Charles Aránguiz = $3.85m
112 / 2921 Néstor Araujo = $4.40m
113 / 2921 Ronald Araújo = $55.00m
114 / 2921 Cameron Archer = $6.60m
115 / 2921 Alphonse Areola = $8.80m
116 / 2921 Jesús Areso = $880Th.
117 / 2921 Matias Arezo = $6.60m
118 / 2921 Santiago Arias = $4.40m
119 / 2921 Adam Armstrong = $16.50m
120 / 2921 Stuart Armstrong = $6.60m
121 / 2921 Marko Arnautović = $6.60m
122 / 2921 Maximilian Arnold = $18.70m
123 / 2921 Kepa Arrizabalaga = $11.00m
124 / 2921 Tolgay Arslan = $2.53m
125 / 2921 Arthur = NaN
126 / 2921 Santiago Arzamendia = $1.98m
127 / 2921 Takuma Asano = $3.30m
128 / 2921 Santiago Ascacíbar = $7.15m
129 / 2921 Sergio Asenjo = $4.40m
130 / 2921 Alejandro Asensio = -
131 / 2921 Marco Asensio = $44.00m
132 / 2921 Harrison Ashby = $330Th.
133 / 2921 Kristoffer Askildsen = $1.98m
134 / 2921 Fisnik Asllani = $440Th.
135 / 2921 Kristjan Asllani = $11.00m
136 / 2921 Iago Aspas = $7.70m
137 / 2921 Lorenz Assignon = $3.30m
138 / 2921 Simon Asta = $990Th.
139 / 2921 Youcef Atal = $16.50m
140 / 2921 Pierre-Emerick Aubameyang = NaN
141 / 2921 Pierre-Emerick Aubameyang = $16.50m
142 / 2921 Emil Audero = $6.60m
143 / 2921 Tommaso Augello = $3.85m
144 / 2921 Jean-Kévin Augustin = $1.65m
145 / 2921 Ludwig Augustinsson = $5.50m
146 / 2921 Serge Aurier = $11.00m
147 / 2921 Ezequiel Ávila = NaN
148 / 2921 Taiwo Awoniyi = $22.00m
149 / 2921 Ramzi Aya = NaN
150 / 2921 Jordan Ayew = $6.60m
151 / 2921 Kaan Ayhan = $5.50m
152 / 2921 Luke Ayling = $4.40m
153 / 2921 Karim Azamoum = $440Th.
154 / 2921 Sardar Azmoun = $24.20m
155 / 2921 César Azpilicueta = $9.90m
156 / 2921 Iddrisu Baba = $3.85m
157 / 2921 Carlos Bacca = $1.10m
158 / 2921 Daniel Bachmann = $2.75m
159 / 2921 Loïc Bade = $13.20m
160 / 2921 Milan Badelj = $1.10m
161 / 2921 Édgar Badía = $3.30m
162 / 2921 Benoît Badiashile = $33.00m
163 / 2921 Youssouph Badji = NaN
164 / 2921 Miguel Baeza = NaN
165 / 2921 Nicola Bagnolini = -
166 / 2921 Issa Bah = $110Th.
167 / 2921 Stéphane Bahoken = $4.40m
168 / 2921 Leon Bailey = $27.50m
169 / 2921 Eric Bailly = NaN
170 / 2921 Dénys Bain = $1.10m
171 / 2921 Stefan Bajic = NaN
172 / 2921 Nedim Bajrami = $11.00m
173 / 2921 Cédric Bakambu = $8.80m
174 / 2921 Abdoulaye Bakayoko = $330Th.
175 / 2921 Tiemoué Bakayoko = $11.00m
176 / 2921 Mitchel Bakker = $15.40m
177 / 2921 Ridle Baku = $22.00m
178 / 2921 Dilane Bakwa = $440Th.
179 / 2921 Tommaso Baldanzi = $880Th.
180 / 2921 Alejandro Balde = $4.40m
181 / 2921 Ibrahima Baldé = $330Th.
182 / 2921 Keita Baldé = $3.30m
183 / 2921 Gareth Bale = $3.30m
184 / 2921 Mikel Balenziaga = NaN
185 / 2921 Leonardo Balerdi = $11.00m
186 / 2921 Iván Balliu = $2.20m
187 / 2921 Fodé Ballo-Touré = $3.85m
188 / 2921 Folarin Balogun = $4.40m
189 / 2921 Abdoulaye Bamba = $550Th.
190 / 2921 Jonathan Bamba = $18.70m
191 / 2921 Patrick Bamford = $17.60m
192 / 2921 Filippo Bandinelli = $3.30m
193 / 2921 Mattia Bani = $1.98m
194 / 2921 Simon Banza = NaN
195 / 2921 Shandon Baptiste = $3.30m
196 / 2921 Antonín Barák = $22.00m
197 / 2921 Barbero = NaN
198 / 2921 Danilo Barbosa = $3.85m
199 / 2921 Barcia = NaN
200 / 2921 Bradley Barcola = $1.10m
201 / 2921 Melvin Bard = $9.90m
202 / 2921 Enis Bardhi = $8.80m
203 / 2921 Francesco Bardi = $660Th.
204 / 2921 Keidi Bare = $4.40m
205 / 2921 Nicolò Barella = $77.00m
206 / 2921 Kike Barja = $3.30m
207 / 2921 Ross Barkley = $13.20m
208 / 2921 Aymen Barkok = NaN
209 / 2921 Ashley Barnes = $1.65m
210 / 2921 Harvey Barnes = $35.20m
211 / 2921 Antonio Barragán = $660Th.
212 / 2921 Leandro Barreiro Martins = NaN
213 / 2921 Ander Barrenetxea = $9.90m
214 / 2921 Jacob Barrett Laursen = NaN
215 / 2921 Musa Barrow = $19.80m
216 / 2921 Abdourahmane Barry = $330Th.
217 / 2921 Marc Bartra = $6.60m
218 / 2921 Daniele Baselli = NaN
219 / 2921 Daniele Baselli = $1.54m
220 / 2921 Toma Bašić = NaN
221 / 2921 Toma Bašić = $8.80m
222 / 2921 Amine Bassi = $1.65m
223 / 2921 Álvaro Bastida = $1.10m
224 / 2921 Alessandro Bastoni = $66.00m
225 / 2921 Simone Bastoni = $8.25m
226 / 2921 Lewis Bate = $1.10m
227 / 2921 Rodrigo Battaglia = $1.98m
228 / 2921 Maximilian Bauer = $1.76m
229 / 2921 Oliver Baumann = $4.95m
230 / 2921 Timo Baumgartl = $5.50m
231 / 2921 Julian Baumgartlinger = $880Th.
232 / 2921 Christoph Baumgartner = $24.20m
233 / 2921 Jon Bautista = NaN
234 / 2921 Mohamed Bayo = $13.20m
235 / 2921 Paul Baysse = $880Th.
236 / 2921 Noah Joel Sarenren Bazee = NaN
237 / 2921 Bebé = NaN
238 / 2921 Ihlas Bebou = $17.60m
239 / 2921 Rodrigo Becão = $6.60m
240 / 2921 Mads Bech Sørensen = $5.50m
241 / 2921 Sheraldo Becker = $7.70m
242 / 2921 Jan Bednarek = $24.20m
243 / 2921 Donny van de Beek = NaN
244 / 2921 Donny van de Beek = NaN
245 / 2921 Asmir Begović = $1.10m
246 / 2921 Valon Behrami = NaN
247 / 2921 Kevin Behrens = $990Th.
248 / 2921 Youcef Belaïli = $2.20m
249 / 2921 Vid Belec = $550Th.
250 / 2921 Ishak Belfodil = $2.42m
251 / 2921 Haris Belkebla = $4.40m
252 / 2921 Stefan Bell = $2.75m
253 / 2921 Armel Bella Kotchap = NaN
254 / 2921 Raoul Bellanova = $7.70m
255 / 2921 Karim Bellarabi = $3.30m
256 / 2921 Jean-Ricner Bellegarde = $8.80m
257 / 2921 Héctor Bellerín = $22.00m
258 / 2921 Jude Bellingham = $88.00m
259 / 2921 George Bello = $3.30m
260 / 2921 Jeanuel Belocian = $550Th.
261 / 2921 Andrea Belotti = $22.00m
262 / 2921 Fran Beltrán = $16.50m
263 / 2921 Hatem Ben Arfa = $1.65m
264 / 2921 Salim Ben Seghir = $440Th.
265 / 2921 Wissam Ben Yedder = $27.50m
266 / 2921 Marco Benassi = NaN
267 / 2921 Marco Benassi = $2.64m
268 / 2921 Darío Benedetto = NaN
269 / 2921 Darío Benedetto = NaN
270 / 2921 László Bénes = NaN
271 / 2921 Walter Benítez = $15.40m
272 / 2921 Iker Benito = $330Th.
273 / 2921 Loris Benito = NaN
274 / 2921 Pedro Benito = NaN
275 / 2921 Filip Benković = $1.98m
276 / 2921 Ismaël Bennacer = $36.30m
277 / 2921 Keanan Bennetts = NaN
278 / 2921 Saïd Benrahma = $27.50m
279 / 2921 Ramy Bensebaini = NaN
280 / 2921 Nabil Bentaleb = $3.85m
281 / 2921 Rodrigo Bentancur = $30.80m
282 / 2921 Rodrigo Bentancur = NaN
283 / 2921 Christian Benteke = $6.60m
284 / 2921 Karim Benzema = $33.00m
285 / 2921 Alessandro Berardi = $55Th.
286 / 2921 Domenico Berardi = $38.50m
287 / 2921 Yuri Berchiche = NaN
288 / 2921 Álex Berenguer = NaN
289 / 2921 Bartosz Bereszyński = $4.40m
290 / 2921 Jóhann Berg Guðmundsson = NaN
291 / 2921 Patrick Berg = $4.95m
292 / 2921 Mitchell van Bergen = $2.20m
293 / 2921 Steven Bergwijn = $19.80m
294 / 2921 Etrit Berisha = $880Th.
295 / 2921 Valon Berisha = $2.75m
296 / 2921 Federico Bernardeschi = $11.00m
297 / 2921 Paul Bernardoni = NaN
298 / 2921 Paul Bernardoni = $7.70m
299 / 2921 Juan Bernat = NaN
300 / 2921 Dimitry Bertaud = $1.65m
301 / 2921 Jason Berthomier = $1.32m
302 / 2921 Nicolò Bertola = $330Th.
303 / 2921 Ryan Bertrand = $3.30m
304 / 2921 Daniel Bessa = $2.75m
305 / 2921 Beto = NaN
306 / 2921 Louis Beyer = NaN
307 / 2921 Bartosz Bialek = $4.40m
308 / 2921 Giulian Biancone = $4.40m
309 / 2921 Ermin Bičakčić = $1.10m
310 / 2921 Mads Bidstrup = NaN
311 / 2921 Pedro Bigas = $1.10m
312 / 2921 El Bilal Touré = NaN
313 / 2921 Jean-Claude Billong = $1.10m
314 / 2921 Luis Binks = $1.65m
315 / 2921 Cristiano Biraghi = $11.00m
316 / 2921 Davide Biraschi = NaN
317 / 2921 Tom Bischof = $1.10m
318 / 2921 Yves Bissouma = $38.50m
319 / 2921 Nathan Bitu Mazala = NaN
320 / 2921 Marco Bizot = $4.95m
321 / 2921 Bjarki Bjarkason = NaN
322 / 2921 Blanco = NaN
323 / 2921 Ludovic Blas = $17.60m
324 / 2921 Danny Blum = $660Th.
325 / 2921 Myron Boadu = $15.40m
326 / 2921 Jérôme Boateng = $3.85m
327 / 2921 Kevin-Prince Boateng = $990Th.
328 / 2921 Antonin Bobichon = NaN
329 / 2921 Herbert Bockhorn = $880Th.
330 / 2921 Jean-Paul Boëtius = $4.40m
331 / 2921 Jeremie Boga = $20.90m
332 / 2921 Jeremie Boga = NaN
333 / 2921 Luka Bogdan = NaN
334 / 2921 Emil Bohinen = $4.40m
335 / 2921 Quentin Boisgard = $1.98m
336 / 2921 Charles Boli = NaN
337 / 2921 Willy Boly = NaN
338 / 2921 Giacomo Bonaventura = $3.30m
339 / 2921 Federico Bonazzoli = $5.50m
340 / 2921 Tarsis Bonga = $330Th.
341 / 2921 Kevin Bonifazi = $4.18m
342 / 2921 Solomon Bonnah = NaN
343 / 2921 Leonardo Bonucci = $6.60m
344 / 2921 Sebastiaan Bornauw = $11.00m
345 / 2921 Rafael Borré = NaN
346 / 2921 Sven Botman = $33.00m
347 / 2921 Denis Bouanga = $8.80m
348 / 2921 Hicham Boudaoui = $7.15m
349 / 2921 Ryad Boudebouz = $2.20m
350 / 2921 Sofiane Boufal = $8.80m
351 / 2921 Farid Boulaya = $3.85m
352 / 2921 Yassine Bounou = $19.80m
353 / 2921 Ismaëlben Boura = NaN
354 / 2921 Mehdi Bourabia = $990Th.
355 / 2921 Benjamin Bourigeaud = $19.80m
356 / 2921 Redwan Bourles = $330Th.
357 / 2921 Edoardo Bove = $1.21m
358 / 2921 Jarrod Bowen = $46.20m
359 / 2921 Dedryck Boyata = $3.85m
360 / 2921 Lucas Boyé = $16.50m
361 / 2921 Domagoj Bradarić = $3.85m
362 / 2921 Bilal Brahimi = $5.50m
363 / 2921 Bilal Brahimi = NaN
364 / 2921 Martin Braithwaite = $6.60m
365 / 2921 Julian Brandt = $27.50m
366 / 2921 Jarrad Branthwaite = $3.30m
367 / 2921 Darko Brašanac = $2.20m
368 / 2921 Lilian Brassier = $4.40m
369 / 2921 Claudio Bravo = $1.10m
370 / 2921 Iker Bravo = $1.10m
371 / 2921 Fabian Bredlow = $550Th.
372 / 2921 Josip Brekalo = NaN
373 / 2921 Josip Brekalo = $16.50m
374 / 2921 Gleison Bremer = $38.50m
375 / 2921 Jimmy Briand = $660Th.
376 / 2921 Yan Brice Eteki = NaN
377 / 2921 Brian Brobbey = NaN
378 / 2921 Armando Broja = NaN
379 / 2921 Dylan Bronn = $4.40m
380 / 2921 John Brooks = $7.70m
381 / 2921 Daniel Brosinski = $550Th.
382 / 2921 Josh Brownhill = $7.70m
383 / 2921 Marcelo Brozović = $44.00m
384 / 2921 Cédric Brunner = $3.52m
385 / 2921 Lewis Brunt = -
386 / 2921 Jacob Bruun Larsen = $6.60m
387 / 2921 Bryan = NaN
388 / 2921 Bryan = NaN
389 / 2921 Ante Budimir = $6.60m
390 / 2921 Emi Buendía = $35.20m
391 / 2921 Osman Bukari = $3.30m
392 / 2921 Aleksander Buksa = $1.32m
393 / 2921 Alessandro Buongiorno = $4.40m
394 / 2921 Sascha Burchert = $440Th.
395 / 2921 Delano Burgzorg = $2.20m
396 / 2921 Jonathan Burkardt = $18.70m
397 / 2921 Roman Bürki = $1.65m
398 / 2921 Dan Burn = NaN
399 / 2921 Dan Burn = NaN
400 / 2921 Maxime Busi = $3.85m
401 / 2921 Gianluca Busio = $9.35m
402 / 2921 Oriol Busquets = $1.32m
403 / 2921 Sergio Busquets = $9.90m
404 / 2921 Jack Butland = $1.98m
405 / 2921 Adrián Butzke = NaN
406 / 2921 Marcin Bułka = $2.20m
407 / 2921 Jamie Bynoe-Gittens = $4.40m
408 / 2921 Sam Byram = $2.20m
409 / 2921 Erick Cabaco = $1.98m
410 / 2921 Willy Caballero = $275Th.
411 / 2921 Rémy Cabella = $7.15m
412 / 2921 Jimmy Cabot = $5.50m
413 / 2921 Jovane Cabral = $6.05m
414 / 2921 Leandro Cabrera = $4.40m
415 / 2921 Liberato Cacace = $2.75m
416 / 2921 Martín Cáceres = $990Th.
417 / 2921 Martín Cáceres = NaN
418 / 2921 Anthony Caci = $7.70m
419 / 2921 Mathieu Cafaro = NaN
420 / 2921 Yannick Cahuzac = $330Th.
421 / 2921 Felipe Caicedo = NaN
422 / 2921 Felipe Caicedo = $1.65m
423 / 2921 Moisés Caicedo = $6.60m
424 / 2921 Marc-Aurèle Caillard = $1.32m
425 / 2921 Jens Cajuste = $5.50m
426 / 2921 Cala = NaN
427 / 2921 Davide Calabria = $27.50m
428 / 2921 Riccardo Calafiori = $3.85m
429 / 2921 Riccardo Calafiori = NaN
430 / 2921 Mattia Caldara = $3.85m
431 / 2921 Calderón = NaN
432 / 2921 Fernando Calero = $1.98m
433 / 2921 Duje Ćaleta-Car = $17.60m
434 / 2921 Hakan Çalhanoğlu = $38.50m
435 / 2921 Daniel Caligiuri = $880Th.
436 / 2921 José Callejón = $1.10m
437 / 2921 Lucas Calodat = $165Th.
438 / 2921 Dominic Calvert-Lewin = $44.00m
439 / 2921 Mahdi Camara = $6.60m
440 / 2921 Mamadou Camara = $220Th.
441 / 2921 Víctor Camarasa = $1.10m
442 / 2921 Eduardo Camavinga = $60.50m
443 / 2921 Eduardo Camavinga = NaN
444 / 2921 Andrea Cambiaso = $5.50m
445 / 2921 José Campaña = $6.60m
446 / 2921 Chem Campbell = NaN
447 / 2921 Emre Can = $17.60m
448 / 2921 Sergio Canales = $22.00m
449 / 2921 Matteo Cancellieri = $3.30m
450 / 2921 João Cancelo = $71.50m
451 / 2921 Fali Candé = $1.65m
452 / 2921 Antonio Candreva = $2.75m
453 / 2921 Sergi Canós = $15.40m
454 / 2921 Álex Cantero = $1.10m
455 / 2921 Todd Cantwell = $19.80m
456 / 2921 Ander Capa = NaN
457 / 2921 Pierrick Capelle = $550Th.
458 / 2921 Leonardo Capezzi = $660Th.
459 / 2921 Étienne Capoue = $3.30m
460 / 2921 Gianluca Caprari = $11.00m
461 / 2921 Francesco Caputo = $2.75m
462 / 2921 Francesco Caputo = NaN
463 / 2921 Maxence Caqueret = $27.50m
464 / 2921 Andrea Carboni = $5.50m
465 / 2921 Isaac Carcelen = $1.65m
466 / 2921 Cárdenas = NaN
467 / 2921 Irvin Cardona = $4.95m
468 / 2921 Igor Carioca = NaN
469 / 2921 Carlo = NaN
470 / 2921 Diego Carlos = $44.00m
471 / 2921 Carmona = NaN
472 / 2921 Yannick Carrasco = $44.00m
473 / 2921 Guido Carrillo = $1.65m
474 / 2921 Dani Carvajal = $19.80m
475 / 2921 William Carvalho = $17.60m
476 / 2921 Nicolò Casale = $8.25m
477 / 2921 Casemiro = NaN
478 / 2921 Matty Cash = $27.50m
479 / 2921 Kiko Casilla = $770Th.
480 / 2921 Moreto Cassamã = $3.30m
481 / 2921 Francesco Cassata = NaN
482 / 2921 Timothy Castagne = $30.80m
483 / 2921 Koen Casteels = $12.10m
484 / 2921 Jean-Charles Castelletto = $4.40m
485 / 2921 Samu Castillejo = $4.40m
486 / 2921 Gonzalo Castro = $550Th.
487 / 2921 Jonny Castro = NaN
488 / 2921 Gaetano Castrovilli = $13.20m
489 / 2921 Danilo Cataldi = $5.50m
490 / 2921 Catena = NaN
491 / 2921 Craig Cathcart = $1.32m
492 / 2921 Edinson Cavani = NaN
493 / 2921 Dani Ceballos = $15.40m
494 / 2921 Pietro Ceccaroni = $2.64m
495 / 2921 Federico Ceccherini = $4.40m
496 / 2921 Emil Ceide = $2.20m
497 / 2921 Zeki Çelik = $19.80m
498 / 2921 Fabien Centonze = $7.70m
499 / 2921 Luca Ceppitelli = $880Th.
500 / 2921 Franco Cervi = $5.50m
501 / 2921 Leonardo César Jardim = NaN
502 / 2921 Damir Ceter = $660Th.
503 / 2921 Julian Chabot = $2.20m
504 / 2921 Julian Chabot = NaN
505 / 2921 El Chadaille Bitshiabu = NaN
506 / 2921 Nassim Chadli = $550Th.
507 / 2921 Kayky Chagas = NaN
508 / 2921 Mehdi Chahiri = NaN
509 / 2921 Trevoh Chalobah = $22.00m
510 / 2921 Calum Chambers = NaN
511 / 2921 Calum Chambers = $11.00m
512 / 2921 Dylan Chambost = $1.32m
513 / 2921 Timothy Chandler = NaN
514 / 2921 Chapela = NaN
515 / 2921 Brendan Chardonnet = $5.50m
516 / 2921 Xavier Chavalerin = $3.85m
517 / 2921 Xavier Chavalerin = NaN
518 / 2921 Justin Che = $3.30m
519 / 2921 Chema = NaN
520 / 2921 Rayan Cherki = $19.80m
521 / 2921 Denis Cheryshev = $2.75m
522 / 2921 Daniel Chesters = $275Th.
523 / 2921 Giorgio Chiellini = $1.65m
524 / 2921 Federico Chiesa = $71.50m
525 / 2921 Ben Chilwell = $41.80m
526 / 2921 Chiquinho = NaN
527 / 2921 Vlad Chiricheș = $1.98m
528 / 2921 Pedro Chirivella = $7.70m
529 / 2921 Joris Chotard = $11.00m
530 / 2921 Hamza Choudhury = $4.40m
531 / 2921 Andreas Christensen = $38.50m
532 / 2921 Max Christiansen = $1.65m
533 / 2921 Carney Chukwuemeka = $6.60m
534 / 2921 Samuel Chukwueze = $22.00m
535 / 2921 Víctor Chust = $2.75m
536 / 2921 Riccardo Ciervo = NaN
537 / 2921 Jasper Cillessen = $3.30m
538 / 2921 Pathé Ciss = $2.20m
539 / 2921 Moustapha Cissé = -
540 / 2921 Giorgio Cittadini = $165Th.
541 / 2921 Ciaran Clark = NaN
542 / 2921 Alexis Claude-Maurice = $6.60m
543 / 2921 Jonathan Clauss = $16.50m
544 / 2921 Carlos Clerc = $2.20m
545 / 2921 Tom Cleverley = $1.98m
546 / 2921 Nathaniel Clyne = $2.20m
547 / 2921 Conor Coady = NaN
548 / 2921 Marcus Coco = $2.20m
549 / 2921 Coke = NaN
550 / 2921 Séamus Coleman = $3.30m
551 / 2921 Álex Collado = $4.40m
552 / 2921 Ebrima Colley = $3.52m
553 / 2921 Omar Colley = $4.95m
554 / 2921 Nathan Collins = $11.00m
555 / 2921 Kingsley Coman = $66.00m
556 / 2921 Eray Cömert = $1.65m
557 / 2921 Santi Comesaña = $4.40m
558 / 2921 Diego Conde = $330Th.
559 / 2921 Aaron Connolly = $6.60m
560 / 2921 Andrea Consigli = $1.65m
561 / 2921 Abdu Conté = $3.30m
562 / 2921 Andrea Conti = $2.75m
563 / 2921 Andrea Conti = NaN
564 / 2921 Liam Cooper = $7.70m
565 / 2921 Lucas Copado = $550Th.
566 / 2921 Diego Coppola = $1.10m
567 / 2921 Francis Coquelin = $5.50m
568 / 2921 Sébastien Corchia = $1.98m
569 / 2921 Sergio Córdova = NaN
570 / 2921 Jack Cork = $1.98m
571 / 2921 Maxwel Cornet = $16.50m
572 / 2921 Maxwel Cornet = NaN
573 / 2921 Jesús Corona = $19.80m
574 / 2921 Ángel Correa = $49.50m
575 / 2921 Joaquín Correa = $25.30m
576 / 2921 Thierry Correia = $8.80m
577 / 2921 Danny da Costa = NaN
578 / 2921 David Pereira da Costa = NaN
579 / 2921 Hélder Costa = NaN
580 / 2921 Hélder Costa = $5.50m
581 / 2921 Jaume Costa = $1.32m
582 / 2921 Benoît Costil = $1.65m
583 / 2921 Vladimír Coufal = $13.20m
584 / 2921 Kalifa Coulibaly = $1.65m
585 / 2921 Lassana Coulibaly = $3.30m
586 / 2921 Mamadou Coulibaly = $2.75m
587 / 2921 Tanguy Coulibaly = $2.75m
588 / 2921 Thibaut Courtois = $66.00m
589 / 2921 Philippe Coutinho = $22.00m
590 / 2921 Philippe Coutinho = NaN
591 / 2921 Nicolas Cozza = $5.50m
592 / 2921 Alessio Cragno = $11.00m
593 / 2921 Aaron Cresswell = $3.30m
594 / 2921 Charlie Cresswell = $3.30m
595 / 2921 Domenico Criscito = $660Th.
596 / 2921 Bryan Cristante = $22.00m
597 / 2921 Cristo = NaN
598 / 2921 Enzo Crivelli = $1.98m
599 / 2921 Domen Črnigoj = $1.65m
600 / 2921 Giovanni Crociata = NaN
601 / 2921 Juan Cruz Armada = NaN
602 / 2921 Juan Cuadrado = $8.80m
603 / 2921 Cucho = NaN
604 / 2921 Marc Cucurella = $30.80m
605 / 2921 Marc Cucurella = NaN
606 / 2921 Jorge Cuenca = $8.80m
607 / 2921 Mickaël Cuisance = NaN
608 / 2921 Mickaël Cuisance = $4.40m
609 / 2921 Luke Cundle = NaN
610 / 2921 Matheus Cunha = $33.00m
611 / 2921 Matheus Cunha = NaN
612 / 2921 Patrick Cutrone = $3.85m
613 / 2921 Wylan Cyprien = $4.95m
614 / 2921 Rafael Czichos = NaN
615 / 2921 Lennart-Marten Czyborra = NaN
616 / 2921 Danilo D'Ambrosio = $3.30m
617 / 2921 Lucas Da Cunha = NaN
618 / 2921 Lucas Da Cunha = $4.40m
619 / 2921 Damien Da Silva = $1.10m
620 / 2921 Moanes Dabbur = $4.40m
621 / 2921 Colin Dagba = NaN
622 / 2921 Finn Dahmen = $1.65m
623 / 2921 Mahmoud Dahoud = $19.80m
624 / 2921 Patson Daka = $22.00m
625 / 2921 Stuart Dallas = $4.40m
626 / 2921 Diogo Dalot = NaN
627 / 2921 Mikkel Damsgaard = $16.50m
628 / 2921 Flavius Daniliuc = $8.80m
629 / 2921 Danilo = NaN
630 / 2921 Kevin Danso = $9.90m
631 / 2921 Dante = NaN
632 / 2921 Ebrima Darboe = $2.20m
633 / 2921 Márton Dárdai = $5.50m
634 / 2921 Sergi Darder = $15.40m
635 / 2921 Vladimír Darida = $2.42m
636 / 2921 Karl Darlow = NaN
637 / 2921 Matteo Darmian = $5.50m
638 / 2921 Josh Dasilva = $11.00m
639 / 2921 Jonathan David = $49.50m
640 / 2921 Alphonso Davies = $77.00m
641 / 2921 Ben Davies = $22.00m
642 / 2921 Tom Davies = $19.80m
643 / 2921 Keinan Davis = $2.75m
644 / 2921 Paweł Dawidowicz = $4.95m
645 / 2921 Craig Dawson = $3.30m
646 / 2921 Kevin De Bruyne = $93.50m
647 / 2921 Konrad De La Fuente = $6.60m
648 / 2921 Sebastien De Maio = NaN
649 / 2921 Tommaso De Nipoti = -
650 / 2921 Rodrigo De Paul = $44.00m
651 / 2921 Mattia De Sciglio = $6.60m
652 / 2921 Lorenzo De Silvestri = $1.65m
653 / 2921 Saulo Decarli = $330Th.
654 / 2921 Troy Deeney = NaN
655 / 2921 Grégoire Defrel = $3.30m
656 / 2921 Alessandro Deiola = $2.20m
657 / 2921 Romain Del Castillo = NaN
658 / 2921 Romain Del Castillo = $3.30m
659 / 2921 Thomas Delaine = $2.20m
660 / 2921 Thomas Delaney = $7.70m
661 / 2921 Thomas Delaney = NaN
662 / 2921 Liam Delap = $1.65m
663 / 2921 Sacha Delaye = $880Th.
664 / 2921 Filippo Delli Carri = $385Th.
665 / 2921 Andy Delort = $14.30m
666 / 2921 Andy Delort = NaN
667 / 2921 Fabian Delph = $1.98m
668 / 2921 Moussa Dembélé = $22.00m
669 / 2921 Ousmane Dembélé = $33.00m
670 / 2921 Yusuf Demir = NaN
671 / 2921 Merih Demiral = $27.50m
672 / 2921 Kerem Demirbay = $16.50m
673 / 2921 Ermedin Demirović = $6.60m
674 / 2921 Diego Demme = $8.80m
675 / 2921 Jason Denayer = $15.40m
676 / 2921 Leander Dendoncker = NaN
677 / 2921 Emmanuel Dennis = $15.40m
678 / 2921 Fabio Depaoli = $3.08m
679 / 2921 Fabio Depaoli = NaN
680 / 2921 Memphis Depay = $38.50m
681 / 2921 Mike van der Hoorn = NaN
682 / 2921 Arthur Desmas = $1.32m
683 / 2921 Sergiño Dest = $19.80m
684 / 2921 Mattia Destro = $3.30m
685 / 2921 Gerard Deulofeu = $16.50m
686 / 2921 Kiernan Dewsbury Hall = NaN
687 / 2921 Jacopo Dezi = NaN
688 / 2921 Samuel Di Carmine = NaN
689 / 2921 Federico Di Francesco = $2.20m
690 / 2921 Giovanni Di Lorenzo = $27.50m
691 / 2921 Ángel Di María = NaN
692 / 2921 Francesco Di Mariano = NaN
693 / 2921 Francesco Di Tacchio = $440Th.
694 / 2921 Boulaye Dia = $13.20m
695 / 2921 Moussa Diaby = $66.00m
696 / 2921 Yadaly Diaby = $220Th.
697 / 2921 Mouctar Diakhaby = $7.70m
698 / 2921 Ibrahim Diakité = $220Th.
699 / 2921 Abdou Diallo = NaN
700 / 2921 Habib Diallo = $13.20m
701 / 2921 Ibrahima Diallo = $11.00m
702 / 2921 Mouhamadou Diarra = NaN
703 / 2921 Stéphane Diarra = $1.65m
704 / 2921 Raphael Dias Belloli = $49.50m
705 / 2921 Rúben Dias = $82.50m
706 / 2921 Krepin Diatta = $14.30m
707 / 2921 Amadou Diawara = $6.60m
708 / 2921 Brahim Díaz = $19.80m
709 / 2921 Javier Díaz = $550Th.
710 / 2921 Luis Díaz = $71.50m
711 / 2921 Víctor Díaz = $770Th.
712 / 2921 Daniel Didavi = $550Th.
713 / 2921 Bamba Dieng = $11.00m
714 / 2921 Eric Dier = $30.80m
715 / 2921 Elhadji Dieye = $275Th.
716 / 2921 Lucas Digne = $27.50m
717 / 2921 Lucas Digne = NaN
718 / 2921 Virgil van Dijk = $60.50m
719 / 2921 Mitchell Dijks = $2.20m
720 / 2921 Javairô Dilrosun = $6.60m
721 / 2921 Javairô Dilrosun = NaN
722 / 2921 Federico Dimarco = $19.80m
723 / 2921 Landry Dimata = $2.42m
724 / 2921 Stole Dimitrievski = $3.30m
725 / 2921 Junior Dina Ebimbe = NaN
726 / 2921 Tristan Dingomé = $1.65m
727 / 2921 Sinaly Diomande = $5.50m
728 / 2921 Issa Diop = $11.00m
729 / 2921 Sofiane Diop = $22.00m
730 / 2921 Andy Diouf = $550Th.
731 / 2921 Assane Dioussé = $1.10m
732 / 2921 Axel Disasi = $18.70m
733 / 2921 Filippo Distefano = -
734 / 2921 Matías Dituro = $1.10m
735 / 2921 Tiago Djaló = $13.20m
736 / 2921 Djené = NaN
737 / 2921 Moussa Djenepo = $11.00m
738 / 2921 Koffi Djidji = $3.30m
739 / 2921 Alexander Djiku = $11.00m
740 / 2921 Bryan Djile Nokoué = NaN
741 / 2921 Berat Djimsiti = $18.70m
742 / 2921 Ouparine Djoco = $1.98m
743 / 2921 Naïs Djouahra = $2.20m
744 / 2921 Marko Dmitrović = $5.50m
745 / 2921 Lewis Dobbin = $330Th.
746 / 2921 Matt Doherty = $13.20m
747 / 2921 Jeremy Doku = $22.00m
748 / 2921 Kasper Dolberg = $18.70m
749 / 2921 Jaume Doménech = $2.20m
750 / 2921 Brandon Domingues = $660Th.
751 / 2921 Carlos Dominguez = $1.10m
752 / 2921 Mario Dominguez = -
753 / 2921 Nicolás Domínguez = $14.30m
754 / 2921 John Donald = NaN
755 / 2921 Lee Dongjun = NaN
756 / 2921 Anastasios Donis = $1.10m
757 / 2921 Gianluigi Donnarumma = NaN
758 / 2921 Niklas Dorsch = $11.00m
759 / 2921 Gabriel Dos Santos = $41.80m
760 / 2921 Jodel Dossou = $1.98m
761 / 2921 Lucas Douath = NaN
762 / 2921 Lohann Doucet = $165Th.
763 / 2921 Abdoulaye Doucouré = $24.20m
764 / 2921 Cheick Doucouré = $16.50m
765 / 2921 Fodé Doucouré = NaN
766 / 2921 Ismaël Doukouré = $1.65m
767 / 2921 Kamory Doumbia = $1.10m
768 / 2921 Moussa Doumbia = $1.32m
769 / 2921 Souleyman Doumbia = $3.30m
770 / 2921 Kieran Dowell = $2.75m
771 / 2921 Bartłomiej Drągowski = $6.60m
772 / 2921 Radu Drăgușin = NaN
773 / 2921 Radu Drăgușin = $3.85m
774 / 2921 Cody Drameh = $3.30m
775 / 2921 Julian Draxler = NaN
776 / 2921 Matthieu Dreyer = $330Th.
777 / 2921 Domingos Duarte = $8.80m
778 / 2921 Óscar Duarte = $880Th.
779 / 2921 Rubén Duarte = $3.30m
780 / 2921 Leo Dubois = $11.00m
781 / 2921 Martin Dúbravka = NaN
782 / 2921 Ondrej Duda = $4.40m
783 / 2921 Jeremy Dudziak = $1.76m
784 / 2921 Shane Duffy = $5.50m
785 / 2921 Unai Dufur = $220Th.
786 / 2921 Denzel Dumfries = $27.50m
787 / 2921 Paul Dummett = NaN
788 / 2921 Alfred Duncan = $9.35m
789 / 2921 Lewis Dunk = $22.00m
790 / 2921 Erik Durm = NaN
791 / 2921 Hugo Duro = NaN
792 / 2921 Hugo Duro = $11.00m
793 / 2921 Jean-Kevin Duverne = $4.40m
794 / 2921 Paulo Dybala = $38.50m
795 / 2921 Edin Džeko = $4.40m
796 / 2921 Enzo Ebosse = $2.20m
797 / 2921 Tyronne Ebuehi = $1.54m
798 / 2921 Ederson = NaN
799 / 2921 Éderson = NaN
800 / 2921 Odsonne Édouard = $18.70m
801 / 2921 CJ Egan-Riley = $880Th.
802 / 2921 Maximilian Eggestein = $8.80m
803 / 2921 Lilian Egloff = $880Th.
804 / 2921 Kingsley Ehizibue = $1.43m
805 / 2921 Julian Eitschberger = -
806 / 2921 Albin Ekdal = $1.10m
807 / 2921 Hugo Ekitike = $30.80m
808 / 2921 Jurgen Ekkelenkamp = $3.85m
809 / 2921 Caleb Ekuban = $1.98m
810 / 2921 Anthony Elanga = NaN
811 / 2921 Alberth Elis = $7.15m
812 / 2921 Harvey Elliott = $24.20m
813 / 2921 Elif Elmas = $26.40m
814 / 2921 Mohamed Elneny = $12.10m
815 / 2921 Aritz Elustondo = $22.00m
816 / 2921 Nico Elvedi = NaN
817 / 2921 Sepe Elye Wahi = NaN
818 / 2921 Mohamed Elyounoussi = $13.20m
819 / 2921 Adri Embarba = $4.40m
820 / 2921 Breel Embolo = NaN
821 / 2921 Emerson = NaN
822 / 2921 Emerson = NaN
823 / 2921 Renaud Emond = NaN
824 / 2921 Youssef En-Nesyri = $27.50m
825 / 2921 Keita Endo = $990Th.
826 / 2921 Wataru Endo = $8.80m
827 / 2921 Christian Eriksen = $22.00m
828 / 2921 Martin Erlic = $6.05m
829 / 2921 Gonzalo Escalante = $2.75m
830 / 2921 Gonzalo Escalante = NaN
831 / 2921 Aarón Escandell = $1.10m
832 / 2921 Sergio Escudero = $1.98m
833 / 2921 Alfonso Espino = $6.60m
834 / 2921 Michael Esser = $440Th.
835 / 2921 Estanis = NaN
836 / 2921 Maxime Estève = $4.95m
837 / 2921 Pervis Estupiñán = $22.00m
838 / 2921 Oghenekaro Etebo = NaN
839 / 2921 Jonny Evans = $4.40m
840 / 2921 Eberechi Eze = $24.20m
841 / 2921 Abdessamad Ezzalzouli = $8.80m
842 / 2921 Łukasz Fabiański = $1.10m
843 / 2921 Fabinho = NaN
844 / 2921 Fábio = NaN
845 / 2921 Cesc Fàbregas = $2.75m
846 / 2921 Wout Faes = $11.00m
847 / 2921 Wahidullah Faghir = $3.30m
848 / 2921 Romain Faivre = $16.50m
849 / 2921 Romain Faivre = NaN
850 / 2921 Radamel Falcao = $2.20m
851 / 2921 Diego Falcinelli = $880Th.
852 / 2921 Wladimiro Falcone = $3.30m
853 / 2921 Fali = NaN
854 / 2921 Paolo Faragò = NaN
855 / 2921 Omar Faraj = $110Th.
856 / 2921 Davide Faraoni = $5.50m
857 / 2921 Mohamed Fares = NaN
858 / 2921 Diego Farias = NaN
859 / 2921 Wuilker Faríñez = $4.40m
860 / 2921 Ömer Faruk Beyaz = NaN
861 / 2921 Noah Fatar = $330Th.
862 / 2921 Ansu Fati = $66.00m
863 / 2921 Julien Faussurier = $330Th.
864 / 2921 Andrea Favilli = NaN
865 / 2921 Federico Fazio = $660Th.
866 / 2921 Aleix Febas = NaN
867 / 2921 Adrian Fein = NaN
868 / 2921 Nabil Fekir = $44.00m
869 / 2921 Felipe = NaN
870 / 2921 Luiz Felipe = $17.60m
871 / 2921 Ohis Felix Uduokhai = NaN
872 / 2921 João Félix = $77.00m
873 / 2921 Kiko Femenía = $2.75m
874 / 2921 Evan Ferguson = $660Th.
875 / 2921 Nathan Ferguson = $2.75m
876 / 2921 Bruno Fernandes = NaN
877 / 2921 Edimilson Fernandes = NaN
878 / 2921 Aitor Fernández = $2.75m
879 / 2921 Álex Fernández = $6.60m
880 / 2921 Álvaro Fernández = $4.40m
881 / 2921 Federico Fernández = NaN
882 / 2921 Fernandinho = $1.65m
883 / 2921 Fernando = $4.40m
884 / 2921 Alex Ferrari = $1.98m
885 / 2921 Salvador Ferrer = $1.43m
886 / 2921 Jordan Ferri = $5.50m
887 / 2921 Riccardo Fiamozzi = $550Th.
888 / 2921 Fidel = NaN
889 / 2921 Karol Fila = $1.32m
890 / 2921 Alfreð Finnbogason = $770Th.
891 / 2921 Luca Fiordilino = $1.10m
892 / 2921 Vincenzo Fiorillo = $330Th.
893 / 2921 Roberto Firmino = $35.20m
894 / 2921 Junior Firpo = $13.20m
895 / 2921 Mark Flekken = $7.70m
896 / 2921 Ashley Fletcher = NaN
897 / 2921 Alexis Flips = $2.75m
898 / 2921 Alessandro Florenzi = $6.05m
899 / 2921 Phil Foden = $99.00m
900 / 2921 Mamadou Fofana = NaN
901 / 2921 Seko Fofana = $27.50m
902 / 2921 Wesley Fofana = $44.00m
903 / 2921 Youssouf Fofana = $19.80m
904 / 2921 Thomas Foket = $5.50m
905 / 2921 Thomas Fontaine = NaN
906 / 2921 José Fontán = $1.10m
907 / 2921 José Fonte = $880Th.
908 / 2921 Fernando Forestieri = NaN
909 / 2921 Pablo Fornals = $24.20m
910 / 2921 Emil Forsberg = $12.10m
911 / 2921 Adam Forshaw = $2.20m
912 / 2921 Marcus Forss = $4.40m
913 / 2921 Fraser Forster = $2.20m
914 / 2921 Philipp Förster = $2.75m
915 / 2921 Francesco Forte = NaN
916 / 2921 Steven Fortes = NaN
917 / 2921 Ben Foster = $330Th.
918 / 2921 Tariqe Fosu = $2.20m
919 / 2921 Timothy Fosu-Mensah = $4.40m
920 / 2921 Dimitri Foulquier = NaN
921 / 2921 Dimitri Foulquier = $4.40m
922 / 2921 Juan Foyth = $27.50m
923 / 2921 Gianluca Frabotta = $2.20m
924 / 2921 Raphael Framberger = $1.65m
925 / 2921 Przemysław Frankowski = $8.80m
926 / 2921 Enric Franquesa = $880Th.
927 / 2921 Fransérgio = NaN
928 / 2921 Ryan Fraser = NaN
929 / 2921 Davide Frattesi = $22.00m
930 / 2921 Fred = NaN
931 / 2921 Ryan Fredericks = $1.65m
932 / 2921 Morten Frendrup = $4.07m
933 / 2921 Remo Freuler = $22.00m
934 / 2921 Marvin Friedrich = NaN
935 / 2921 Marvin Friedrich = NaN
936 / 2921 Jeremie Frimpong = $22.00m
937 / 2921 Christian Früchtl = $660Th.
938 / 2921 Jorge de Frutos = $11.00m
939 / 2921 Leo Fuhr Hjelde = NaN
940 / 2921 Chris Führich = $4.40m
941 / 2921 Angelo Fulgini = $13.20m
942 / 2921 Marius Funk = $330Th.
943 / 2921 Stephan Fürstner = $83Th.
944 / 2921 Matteo Gabbia = $6.60m
945 / 2921 Manolo Gabbiadini = $4.40m
946 / 2921 Mijat Gaćinović = NaN
947 / 2921 Gianluca Gaetano = NaN
948 / 2921 Luca Gagliano = $275Th.
949 / 2921 Roberto Gagliardini = $8.80m
950 / 2921 Riccardo Gagliolo = $880Th.
951 / 2921 Javi Galán = $13.20m
952 / 2921 Iñigo Ruiz de Galarreta = $1.98m
953 / 2921 Nicolas Galazzi = NaN
954 / 2921 Pablo Galdames Millán = NaN
955 / 2921 Thiago Galhardo = $2.20m
956 / 2921 Conor Gallagher = NaN
957 / 2921 Gauthier Gallon = $2.75m
958 / 2921 Cristian Gamboa = $770Th.
959 / 2921 Kévin Gameiro = $2.20m
960 / 2921 Idrissa Gana Gueye = NaN
961 / 2921 Ignatius Ganago = $5.50m
962 / 2921 Silvère Ganvoula M'Boussy = NaN
963 / 2921 Álvaro García = $6.60m
964 / 2921 Dani García = NaN
965 / 2921 David García = $22.00m
966 / 2921 Eric García = $19.80m
967 / 2921 Francisco Garcia = $8.25m
968 / 2921 Joan García = $440Th.
969 / 2921 Manu García = $3.30m
970 / 2921 Raúl García = NaN
971 / 2921 Rubén García = $8.80m
972 / 2921 Saúl García = $660Th.
973 / 2921 Unai García = $1.65m
974 / 2921 Alejandro Garnacho = NaN
975 / 2921 Mario Gaspar = $3.30m
976 / 2921 Djeidi Gassama = NaN
977 / 2921 Johan Gastien = $880Th.
978 / 2921 Dwight Gayle = NaN
979 / 2921 Jean-Philippe Gbamin = NaN
980 / 2921 David de Gea = NaN
981 / 2921 Linus Gechter = $4.95m
982 / 2921 Dennis Geiger = $9.90m
983 / 2921 Joe Gelhardt = $6.60m
984 / 2921 Luca Gemello = $330Th.
985 / 2921 Yannick Gerhardt = $6.60m
986 / 2921 Valère Germain = $2.75m
987 / 2921 Gerson = NaN
988 / 2921 Willem Geubbels = $4.40m
989 / 2921 Hamadi Al Ghaddioui = NaN
990 / 2921 Ismaël Gharbi = NaN
991 / 2921 Anwar El Ghazi = NaN
992 / 2921 Anwar El Ghazi = $9.90m
993 / 2921 Lamine Ghezali = $275Th.
994 / 2921 Paolo Ghiglione = $2.20m
995 / 2921 Saman Ghoddos = $2.75m
996 / 2921 Faouzi Ghoulam = $1.54m
997 / 2921 Dimitris Giannoulis = $5.50m
998 / 2921 Morgan Gibbs-White = NaN
999 / 2921 Ben Gibson = $6.60m
1000 / 2921 Niko Gießelmann = $2.53m
1001 / 2921 Rafał Gikiewicz = $1.32m
1002 / 2921 Óscar Gil = $3.30m
1003 / 2921 Mario Gila = $550Th.
1004 / 2921 Billy Gilmour = NaN
1005 / 2921 Daniel Ginczek = NaN
1006 / 2921 Matthias Ginter = NaN
1007 / 2921 Nicholas Gioacchini = $1.65m
1008 / 2921 Sebastian Giovinco = $550Th.
1009 / 2921 Jimmy Giraudon = NaN
1010 / 2921 Andrei Girotto = $5.50m
1011 / 2921 Olivier Giroud = $3.85m
1012 / 2921 Serge Gnabry = $71.50m
1013 / 2921 Ben Godfrey = $22.00m
1014 / 2921 Diego Godín = NaN
1015 / 2921 Edoardo Goldaniga = $1.10m
1016 / 2921 Edoardo Goldaniga = NaN
1017 / 2921 Aleksandr Golovin = $27.50m
1018 / 2921 André Gomes = $19.80m
1019 / 2921 Angel Gomes = $7.70m
1020 / 2921 Toti Gomes = NaN
1021 / 2921 Dani Gómez = $2.75m
1022 / 2921 Joe Gomez = $19.80m
1023 / 2921 Maxi Gómez = $13.20m
1024 / 2921 Moi Gómez = $8.80m
1025 / 2921 Papu Gómez = NaN
1026 / 2921 Sergi Gómez = $3.30m
1027 / 2921 Alfred Gomis = $8.80m
1028 / 2921 Maxime Gonalons = $1.98m
1029 / 2921 Vagner Gonçalves = NaN
1030 / 2921 Cedric Gondo = NaN
1031 / 2921 Álvaro González = $2.75m
1032 / 2921 Diego González = $2.20m
1033 / 2921 Édgar González = $6.60m
1034 / 2921 Giovanni González = $1.65m
1035 / 2921 Nicolás González = $22.00m
1036 / 2921 Nicolás González = $27.50m
1037 / 2921 Peter González = $1.10m
1038 / 2921 Roberto González = NaN
1039 / 2921 Charlie Goode = $990Th.
1040 / 2921 Anthony Gordon = $22.00m
1041 / 2921 Kaide Gordon = $1.65m
1042 / 2921 Leon Goretzka = $71.50m
1043 / 2921 Gori = NaN
1044 / 2921 Andoni Gorosabel = $11.00m
1045 / 2921 Robin Gosens = NaN
1046 / 2921 Robin Gosens = $30.80m
1047 / 2921 Dan Gosling = $1.10m
1048 / 2921 Amine Gouiri = $46.20m
1049 / 2921 Jeffrey Gouweleeuw = $3.85m
1050 / 2921 Jonathan Gradit = $4.95m
1051 / 2921 Jens Grahl = NaN
1052 / 2921 Alberto Grassi = $2.75m
1053 / 2921 Jaume Grau = NaN
1054 / 2921 Andrew Gravillon = $7.15m
1055 / 2921 Demarai Gray = $24.20m
1056 / 2921 Adrian Grbić = NaN
1057 / 2921 Ivo Grbić = $3.85m
1058 / 2921 Jack Grealish = $77.00m
1059 / 2921 Etienne Green = $7.70m
1060 / 2921 Julian Green = $1.10m
1061 / 2921 Mason Greenwood = NaN
1062 / 2921 Sam Greenwood = $2.75m
1063 / 2921 Michael Gregoritsch = $4.95m
1064 / 2921 Dominik Greif = $2.20m
1065 / 2921 Clément Grenier = $1.10m
1066 / 2921 Sebastian Griesbeck = $770Th.
1067 / 2921 Sebastian Griesbeck = NaN
1068 / 2921 Antoine Griezmann = NaN
1069 / 2921 Antoine Griezmann = $38.50m
1070 / 2921 Vincenzo Grifo = $13.20m
1071 / 2921 Lennart Grill = $1.10m
1072 / 2921 Florian Grillitsch = $17.60m
1073 / 2921 Arnaut Groeneveld = NaN
1074 / 2921 Pascal Groß = $6.60m
1075 / 2921 Carlos Gruezo = $3.52m
1076 / 2921 Vicente Guaita = $1.65m
1077 / 2921 Andrés Guardado = $1.65m
1078 / 2921 Sergi Guardiola = $3.30m
1079 / 2921 Nemanja Gudelj = $4.40m
1080 / 2921 Gabriel Gudmundsson = $6.60m
1081 / 2921 Noah Guede-Nadje = NaN
1082 / 2921 Gonçalo Guedes = $44.00m
1083 / 2921 Marc Guéhi = $35.20m
1084 / 2921 Mattéo Guendouzi = $27.50m
1085 / 2921 Yanis Guermouche = $275Th.
1086 / 2921 Raphaël Guerreiro = $27.50m
1087 / 2921 Evann Guessand = $3.30m
1088 / 2921 Ander Guevara = $5.50m
1089 / 2921 Lamine Gueye = NaN
1090 / 2921 Pape Gueye = $13.20m
1091 / 2921 John Guidetti = $1.10m
1092 / 2921 Josuha Guilavogui = $3.85m
1093 / 2921 Josuha Guilavogui = NaN
1094 / 2921 Frederic Guilbert = $7.70m
1095 / 2921 Hugo Guillamón = $22.00m
1096 / 2921 Bruno Guimarães = NaN
1097 / 2921 Bruno Guimarães = NaN
1098 / 2921 Sehrou Guirassy = $11.00m
1099 / 2921 Péter Gulácsi = $8.80m
1100 / 2921 Manuel Gulde = $1.87m
1101 / 2921 Gerard Gumbau = $3.30m
1102 / 2921 Robert Gumny = $2.75m
1103 / 2921 İlkay Gündoğan = $27.50m
1104 / 2921 Angus Gunn = $3.30m
1105 / 2921 Christian Günter = $13.20m
1106 / 2921 Koray Günter = $5.50m
1107 / 2921 Lasse Günther = $1.10m
1108 / 2921 Jon Guridi = $1.32m
1109 / 2921 Malo Gusto = $13.20m
1110 / 2921 Raúl Guti = $4.40m
1111 / 2921 Marcos Mauro López Gutiérrez = NaN
1112 / 2921 Miguel Gutiérrez = $6.60m
1113 / 2921 Albert Guðmundsson = $2.64m
1114 / 2921 Joško Gvardiol = $38.50m
1115 / 2921 Emmanuel Gyasi = $2.75m
1116 / 2921 Norbert Gyömbér = $1.10m
1117 / 2921 Erling Haaland = $165.00m
1118 / 2921 Nicolas Haas = $3.30m
1119 / 2921 Janik Haberer = $3.85m
1120 / 2921 Alexander Hack = $2.75m
1121 / 2921 Robin Hack = $2.20m
1122 / 2921 Munir El Haddadi = $8.80m
1123 / 2921 André Hahn = $2.20m
1124 / 2921 Amadou Haidara = $26.40m
1125 / 2921 Massadio Haïdara = $2.20m
1126 / 2921 Oualid El Hajjam = $1.10m
1127 / 2921 Achraf Hakimi = NaN
1128 / 2921 Marcel Halstenberg = $5.50m
1129 / 2921 Pierre-Yves Hamel = NaN
1130 / 2921 Pierre-Yves Hamel = $1.32m
1131 / 2921 Romain Hamouma = $1.10m
1132 / 2921 Samir Handanović = $2.20m
1133 / 2921 Grant Hanley = $3.85m
1134 / 2921 Ridgeciano Haps = $1.98m
1135 / 2921 Genki Haraguchi = $2.75m
1136 / 2921 Amine Harit = $11.00m
1137 / 2921 Varazdat Haroyan = $1.32m
1138 / 2921 Jack Harrison = $19.80m
1139 / 2921 Abdou Harroui = $3.30m
1140 / 2921 Luis Hartwig = $220Th.
1141 / 2921 Makoto Hasebe = NaN
1142 / 2921 Hans Hateboer = $14.30m
1143 / 2921 Kortney Hause = $4.40m
1144 / 2921 Kai Havertz = $77.00m
1145 / 2921 Isaac Hayden = NaN
1146 / 2921 Eden Hazard = $13.20m
1147 / 2921 Thorgan Hazard = $16.50m
1148 / 2921 Jonas Hector = $3.85m
1149 / 2921 Hwang Hee-chan = NaN
1150 / 2921 Hwang Hee-chan = NaN
1151 / 2921 Silvan Hefti = $5.50m
1152 / 2921 Dominique Heintz = $2.75m
1153 / 2921 Dominique Heintz = NaN
1154 / 2921 Jordan Henderson = $16.50m
1155 / 2921 Liam Henderson = $3.08m
1156 / 2921 Jeff Hendrick = NaN
1157 / 2921 Wayne Hennessey = $550Th.
1158 / 2921 Benjamin Henrichs = $15.40m
1159 / 2921 Henrique = NaN
1160 / 2921 Dalbert Henrique = NaN
1161 / 2921 Luis Henrique = $6.60m
1162 / 2921 Matheus Henrique = $12.10m
1163 / 2921 Rico Henry = $22.00m
1164 / 2921 Thomas Henry = $5.50m
1165 / 2921 Christophe Hérelle = $4.40m
1166 / 2921 Jérôme Hergault = $275Th.
1167 / 2921 Kike Hermoso = $330Th.
1168 / 2921 Mario Hermoso = $22.00m
1169 / 2921 Aridane Hernández = $1.65m
1170 / 2921 Lucas Hernández = $55.00m
1171 / 2921 Luis Hernández = $880Th.
1172 / 2921 Mario Hernández = $880Th.
1173 / 2921 Theo Hernández = $60.50m
1174 / 2921 Hernani = $3.30m
1175 / 2921 Ander Herrera = NaN
1176 / 2921 Héctor Herrera = $5.50m
1177 / 2921 Sergio Herrera = $8.80m
1178 / 2921 Yangel Herrera = $15.40m
1179 / 2921 Patrick Herrmann = NaN
1180 / 2921 Son Heung-min = NaN
1181 / 2921 Daan Heymans = NaN
1182 / 2921 Aaron Hickey = $19.80m
1183 / 2921 Piero Hincapié = $18.70m
1184 / 2921 Martin Hinteregger = NaN
1185 / 2921 Marwin Hitz = $1.10m
1186 / 2921 Ki-Jana Hoever = NaN
1187 / 2921 Nicolas Höfler = $2.75m
1188 / 2921 Jonas Hofmann = NaN
1189 / 2921 Rob Holding = $11.00m
1190 / 2921 Lucas Höler = $7.15m
1191 / 2921 Mason Holgate = $19.80m
1192 / 2921 Gerrit Holtmann = $4.95m
1193 / 2921 Martin Hongla = $4.40m
1194 / 2921 Franck Honorat = $11.00m
1195 / 2921 Justin Hoogma = NaN
1196 / 2921 Sydney van Hooijdonk = NaN
1197 / 2921 Matthew Hoppe = $1.10m
1198 / 2921 Jannes Horn = $1.98m
1199 / 2921 Timo Horn = $2.20m
1200 / 2921 Fraser Hornby = $1.10m
1201 / 2921 Cédric Hountondji = $3.30m
1202 / 2921 Lukáš Hrádecký = $5.50m
1203 / 2921 Branimir Hrgota = $2.20m
1204 / 2921 Petko Hristov = $1.10m
1205 / 2921 Ajdin Hrustic = NaN
1206 / 2921 Timo Hübers = $8.25m
1207 / 2921 Benjamin Hübner = $1.10m
1208 / 2921 Callum Hudson-Odoi = $27.50m
1209 / 2921 Will Hughes = $6.60m
1210 / 2921 Mats Hummels = $7.15m
1211 / 2921 Elseid Hysaj = $5.50m
1212 / 2921 Suk Hyun-jun = NaN
1213 / 2921 Pierre Højbjerg = $44.00m
1214 / 2921 Iago = NaN
1215 / 2921 Robert Ibáñez = NaN
1216 / 2921 Roger Ibanez = $25.30m
1217 / 2921 Vicente Iborra = $2.20m
1218 / 2921 Zlatan Ibrahimović = $3.30m
1219 / 2921 Mauro Icardi = NaN
1220 / 2921 Adam Idah = $3.30m
1221 / 2921 Oussama Idrissi = NaN
1222 / 2921 Oussama Idrissi = $4.40m
1223 / 2921 Iglesias = NaN
1224 / 2921 Borja Iglesias = $16.50m
1225 / 2921 Jonathan Iglesias = NaN
1226 / 2921 Juan Ignacio Ramírez = NaN
1227 / 2921 Igor = NaN
1228 / 2921 Kelechi Iheanacho = $22.00m
1229 / 2921 Danylo Ihnatenko = NaN
1230 / 2921 Jonathan Ikone = NaN
1231 / 2921 Jonathan Ikone = $19.80m
1232 / 2921 Ivan Ilić = $15.40m
1233 / 2921 Josip Iličić = $1.10m
1234 / 2921 Asier Illarramendi = $3.30m
1235 / 2921 Stefan Ilsanker = NaN
1236 / 2921 Ciro Immobile = $29.70m
1237 / 2921 Burak İnce = $1.65m
1238 / 2921 Danny Ings = $22.00m
1239 / 2921 Marcus Ingvartsen = NaN
1240 / 2921 Marcus Ingvartsen = $2.75m
1241 / 2921 Bonke Innocent = $2.20m
1242 / 2921 Lorenzo Insigne = $27.50m
1243 / 2921 Nikita Iosifov = $1.10m
1244 / 2921 Tim Iroegbunam = $2.75m
1245 / 2921 Alexander Isak = $33.00m
1246 / 2921 Isco = NaN
1247 / 2921 Wilson Isidor = NaN
1248 / 2921 Ardian Ismajli = $1.98m
1249 / 2921 Hiroki Ito = $4.95m
1250 / 2921 Cedric Itten = NaN
1251 / 2921 Gian-Luca Itter = NaN
1252 / 2921 Alex Iwobi = $19.80m
1253 / 2921 Armando Izzo = $3.08m
1254 / 2921 Nicolas Jackson = $550Th.
1255 / 2921 Lee Jae-sung = NaN
1256 / 2921 Paul Jaeckel = $5.50m
1257 / 2921 Mato Jajalo = $440Th.
1258 / 2921 Kristijan Jakić = NaN
1259 / 2921 Ismail Jakobs = $7.70m
1260 / 2921 Marin Jakoliš = $1.32m
1261 / 2921 Abdel Jalil Medioub = NaN
1262 / 2921 Daniel James = NaN
1263 / 2921 Daniel James = $19.80m
1264 / 2921 Reece James = $66.00m
1265 / 2921 Vitaly Janelt = $15.40m
1266 / 2921 Jakub Jankto = $4.40m
1267 / 2921 Pontus Jansson = $7.70m
1268 / 2921 Tony Jantschke = NaN
1269 / 2921 Adnan Januzaj = $13.20m
1270 / 2921 Paweł Jaroszyński = $660Th.
1271 / 2921 Jason = NaN
1272 / 2921 Jason = NaN
1273 / 2921 Dennis Jastrzembski = NaN
1274 / 2921 Luis Javier Suárez = NaN
1275 / 2921 Corentin Jean = $1.32m
1276 / 2921 Jemerson = NaN
1277 / 2921 Fredrik Jensen = $1.65m
1278 / 2921 Mathias Jensen = $11.00m
1279 / 2921 Moritz Jenz = $2.75m
1280 / 2921 Gabriel Jesus = $55.00m
1281 / 2921 Juan Jesus = $4.40m
1282 / 2921 Álvaro Jiménez = NaN
1283 / 2921 Raúl Jiménez = NaN
1284 / 2921 Joaquín = NaN
1285 / 2921 Joelinton = NaN
1286 / 2921 Jofre = NaN
1287 / 2921 Marco John = $2.75m
1288 / 2921 Dennis Johnsen = $1.54m
1289 / 2921 Ben Johnson = $9.90m
1290 / 2921 Curtis Jones = $30.80m
1291 / 2921 Phil Jones = NaN
1292 / 2921 Frenkie de Jong = $66.00m
1293 / 2921 Luuk de Jong = $4.40m
1294 / 2921 Joan Jordán = $24.20m
1295 / 2921 Kaio Jorge = $7.70m
1296 / 2921 Jorginho = NaN
1297 / 2921 Josan = NaN
1298 / 2921 Willian José = $13.20m
1299 / 2921 Joselu = NaN
1300 / 2921 Josema = NaN
1301 / 2921 Lenny Joseph = $550Th.
1302 / 2921 Diogo Jota = $66.00m
1303 / 2921 Stevan Jovetić = $2.75m
1304 / 2921 Luka Jović = $17.60m
1305 / 2921 Miguel Juan Llambrich = $880Th.
1306 / 2921 José Juan Macías = NaN
1307 / 2921 Juanmi = NaN
1308 / 2921 Gideon Jung = $770Th.
1309 / 2921 Flavio Junior Bianchi = NaN
1310 / 2921 Hamed Junior Traorè = NaN
1311 / 2921 Lago Junior = NaN
1312 / 2921 Vinicius Júnior = $110.00m
1313 / 2921 James Justin = $27.50m
1314 / 2921 Jutglà = NaN
1315 / 2921 Jens Jønsson = $4.40m
1316 / 2921 Mathias Jørgensen = $1.32m
1317 / 2921 Ozan Kabak = $11.00m
1318 / 2921 Christian Kabasele = $3.30m
1319 / 2921 Issa Kaboré = $4.40m
1320 / 2921 Anton Kade = $825Th.
1321 / 2921 Pavel Kadeřábek = $5.50m
1322 / 2921 Tino Kadewere = $6.60m
1323 / 2921 Florian Kainz = $3.85m
1324 / 2921 Gaël Kakuta = $7.70m
1325 / 2921 Sasa Kalajdzic = $27.50m
1326 / 2921 Arnaud Kalimuendo = NaN
1327 / 2921 Arnaud Kalimuendo = $19.80m
1328 / 2921 Nikola Kalinić = NaN
1329 / 2921 Yayah Kallon = $1.65m
1330 / 2921 Felix Kalu Nmecha = NaN
1331 / 2921 Samuel Kalu = $3.30m
1332 / 2921 Samuel Kalu = NaN
1333 / 2921 Pierre Kalulu = $30.80m
1334 / 2921 Daichi Kamada = NaN
1335 / 2921 Boubacar Kamara = $27.50m
1336 / 2921 Hassane Kamara = $4.40m
1337 / 2921 Hassane Kamara = NaN
1338 / 2921 Dimitrije Kamenović = $1.65m
1339 / 2921 Kevin Kampl = $6.60m
1340 / 2921 Jean-Armel Kana-Biyik = $550Th.
1341 / 2921 Nordine Kandil = $440Th.
1342 / 2921 Harry Kane = $99.00m
1343 / 2921 Lee Kangin = NaN
1344 / 2921 N'Golo Kanté = $44.00m
1345 / 2921 Stefanos Kapino = $770Th.
1346 / 2921 Atakan Karazor = $4.95m
1347 / 2921 Rick Karsdorp = $14.30m
1348 / 2921 Denso Kasius = $2.20m
1349 / 2921 Grigoris Kastanos = $1.65m
1350 / 2921 Silas Katompa = $16.50m
1351 / 2921 Noah Katterbach = NaN
1352 / 2921 Eiji Kawashima = $220Th.
1353 / 2921 Edo Kayembe = $3.30m
1354 / 2921 Moise Kean = $22.00m
1355 / 2921 Moise Kean = $26.40m
1356 / 2921 Michael Keane = $22.00m
1357 / 2921 Ilan Kebbal = $4.95m
1358 / 2921 Wajdi Kechrida = $880Th.
1359 / 2921 Thilo Kehrer = NaN
1360 / 2921 Habib Keita = $880Th.
1361 / 2921 Naby Keïta = $27.50m
1362 / 2921 Yannik Keitel = $4.40m
1363 / 2921 Caoimhín Kelleher = $8.80m
1364 / 2921 Marc-Oliver Kempf = $4.95m
1365 / 2921 Marc-Oliver Kempf = NaN
1366 / 2921 Kenedy = NaN
1367 / 2921 Jonjoe Kenny = $4.40m
1368 / 2921 Dimitrios Keramitsis = -
1369 / 2921 Franck Kessié = $49.50m
1370 / 2921 Saîf-Eddine Khaoui = $1.65m
1371 / 2921 Wahbi Khazri = $4.40m
1372 / 2921 Rani Khedira = $6.60m
1373 / 2921 Kike = NaN
1374 / 2921 Luca Kilian = NaN
1375 / 2921 Luca Kilian = $4.95m
1376 / 2921 Max Kilman = NaN
1377 / 2921 Joshua Kimmich = $88.00m
1378 / 2921 Presnel Kimpembe = NaN
1379 / 2921 Joshua King = $8.80m
1380 / 2921 Jakub Kiwior = $3.30m
1381 / 2921 Sofian Kiyine = $2.31m
1382 / 2921 Simon Kjær = NaN
1383 / 2921 Kristoffer Klaesson = $2.20m
1384 / 2921 Philipp Klement = NaN
1385 / 2921 Mateusz Klich = $3.30m
1386 / 2921 Thibault Klidje = $330Th.
1387 / 2921 Mateo Klimowicz = $2.20m
1388 / 2921 Fabian Klos = $440Th.
1389 / 2921 Lukas Klostermann = $19.80m
1390 / 2921 Justin Kluivert = $16.50m
1391 / 2921 Lukas Klünter = $1.32m
1392 / 2921 Ansgar Knauff = NaN
1393 / 2921 Ansgar Knauff = NaN
1394 / 2921 Robin Knoche = $5.50m
1395 / 2921 Gregor Kobel = $24.20m
1396 / 2921 Robin Koch = $17.60m
1397 / 2921 Samuel Koeberlé = $165Th.
1398 / 2921 Philippe Koffi = NaN
1399 / 2921 Dominik Kohr = $5.50m
1400 / 2921 Koba Koindredi = $1.10m
1401 / 2921 Koke = NaN
1402 / 2921 Aleksandr Kokorin = $880Th.
1403 / 2921 Aleksandar Kolarov = $550Th.
1404 / 2921 Sead Kolašinac = NaN
1405 / 2921 Sead Kolašinac = $3.30m
1406 / 2921 Luca Koleosho = -
1407 / 2921 Randal Kolo Muani = $17.60m
1408 / 2921 Timothée Kolodziejczak = $2.75m
1409 / 2921 Ghislain Konan = $7.70m
1410 / 2921 Ibrahima Konaté = $44.00m
1411 / 2921 Geoffrey Kondogbia = $22.00m
1412 / 2921 Ibrahima Koné = $4.95m
1413 / 2921 Kouadio Koné = NaN
1414 / 2921 Youssouf Koné = $2.20m
1415 / 2921 Ezri Konsa = $24.20m
1416 / 2921 Teun Koopmeiners = $27.50m
1417 / 2921 Laurent Koscielny = NaN
1418 / 2921 Odilon Kossonou = NaN
1419 / 2921 Filip Kostić = NaN
1420 / 2921 Rominigue Kouamé = $5.50m
1421 / 2921 Nianzou Kouassi = NaN
1422 / 2921 Kalidou Koulibaly = $38.50m
1423 / 2921 Jules Koundé = $66.00m
1424 / 2921 Christos Kourfalidis = $138Th.
1425 / 2921 Boubakar Kouyaté = NaN
1426 / 2921 Cheikhou Kouyaté = $4.40m
1427 / 2921 Mateo Kovačić = $46.20m
1428 / 2921 Viktor Kovalenko = $3.30m
1429 / 2921 Emil Krafth = NaN
1430 / 2921 Alex Král = $7.70m
1431 / 2921 Andrej Kramarić = $13.20m
1432 / 2921 Christoph Kramer = NaN
1433 / 2921 Jean-Philippe Krasso = NaN
1434 / 2921 Julian Kristoffersen = NaN
1435 / 2921 Toni Kroos = $22.00m
1436 / 2921 Florian Krüger = $1.98m
1437 / 2921 Tim Krul = $2.20m
1438 / 2921 Rade Krunić = $9.90m
1439 / 2921 Max Kruse = $3.85m
1440 / 2921 Max Kruse = NaN
1441 / 2921 Lukas Kübler = $2.53m
1442 / 2921 Takefusa Kubo = $8.25m
1443 / 2921 Juraj Kucka = $1.98m
1444 / 2921 Mykola Kukharevych = NaN
1445 / 2921 Dejan Kulusevski = $44.00m
1446 / 2921 Dejan Kulusevski = NaN
1447 / 2921 Marash Kumbulla = $17.60m
1448 / 2921 Fabian Kunze = $1.10m
1449 / 2921 Enock Kwateng = $3.30m
1450 / 2921 Grejohn Kyei = $1.32m
1451 / 2921 Giorgos Kyriakopoulos = $5.50m
1452 / 2921 Andrea La Mantia = $1.43m
1453 / 2921 Gaëtan Laborde = NaN
1454 / 2921 Gaëtan Laborde = $19.80m
1455 / 2921 Alexandre Lacazette = $16.50m
1456 / 2921 Tom Lacoux = $880Th.
1457 / 2921 Lenny Lacroix = $1.10m
1458 / 2921 Maxence Lacroix = $22.00m
1459 / 2921 Alban Lafont = $17.60m
1460 / 2921 Víctor Laguardia = $2.20m
1461 / 2921 Konrad Laimer = $30.80m
1462 / 2921 Stefan Lainer = NaN
1463 / 2921 Diego Lainez = $2.75m
1464 / 2921 Adam Lallana = $2.20m
1465 / 2921 Vassilis Lambropoulos = NaN
1466 / 2921 Érik Lamela = $17.60m
1467 / 2921 Didier Lamkel Zé = $3.85m
1468 / 2921 Sam Lammers = NaN
1469 / 2921 Sam Lammers = NaN
1470 / 2921 Tariq Lamptey = $19.80m
1471 / 2921 Manuel Lanzini = $13.20m
1472 / 2921 Aymeric Laporte = $49.50m
1473 / 2921 Julien Laporte = $3.30m
1474 / 2921 Yasser Larouci = $2.75m
1475 / 2921 Kevin Lasagna = $3.85m
1476 / 2921 Jamaal Lascelles = NaN
1477 / 2921 Bryan Lasme = $1.10m
1478 / 2921 Paul Lasne = $550Th.
1479 / 2921 Latasa = NaN
1480 / 2921 Toni Lato = $1.65m
1481 / 2921 Armand Lauriente = $7.70m
1482 / 2921 Darko Lazović = $4.40m
1483 / 2921 Manuel Lazzari = $16.50m
1484 / 2921 Théo Le Bris = $440Th.
1485 / 2921 Jérémy Le Douaron = $3.30m
1486 / 2921 Enzo Le Fée = $6.60m
1487 / 2921 Vincent Le Goff = $1.10m
1488 / 2921 Maxime Le Marchand = $1.32m
1489 / 2921 Robin Le Normand = $33.00m
1490 / 2921 Patrick Leal = -
1491 / 2921 Rafael Leão = $77.00m
1492 / 2921 Jean-Louis Leca = $550Th.
1493 / 2921 Jeremías Ledesma = $6.60m
1494 / 2921 Pierre Lees-Melou = $5.50m
1495 / 2921 Wu Lei = NaN
1496 / 2921 Maxim Leitsch = $5.50m
1497 / 2921 Lucas Leiva = $1.10m
1498 / 2921 Florian Lejeune = $2.20m
1499 / 2921 Iñigo Lekue = NaN
1500 / 2921 Thomas Lemar = $44.00m
1501 / 2921 Félix Lemaréchal = $660Th.
1502 / 2921 Mario Lemina = $9.90m
1503 / 2921 Fabien Lemoine = $660Th.
1504 / 2921 Tim Lemperle = $2.20m
1505 / 2921 Ruben Lendinez = NaN
1506 / 2921 Clément Lenglet = $13.20m
1507 / 2921 Aaron Lennon = $1.10m
1508 / 2921 Bernd Leno = $9.90m
1509 / 2921 Christopher Lenz = NaN
1510 / 2921 Léo Leroy = $1.65m
1511 / 2921 Alexandre Letellier = NaN
1512 / 2921 Robert Lewandowski = $49.50m
1513 / 2921 Jamie Leweling = $4.95m
1514 / 2921 Jamal Lewis = NaN
1515 / 2921 Luca Lezzerini = $550Th.
1516 / 2921 Ben Lhassine Kone = NaN
1517 / 2921 Yanis Lhery = $440Th.
1518 / 2921 Dimitri Liénard = $770Th.
1519 / 2921 Philipp Lienhart = $19.80m
1520 / 2921 Matthijs de Ligt = $77.00m
1521 / 2921 Isaac Lihadji = $3.85m
1522 / 2921 Anderson Lima = NaN
1523 / 2921 Andreas Linde = $990Th.
1524 / 2921 Victor Lindelöf = NaN
1525 / 2921 Jesper Lindstrøm = NaN
1526 / 2921 Karol Linetty = $3.85m
1527 / 2921 Jesse Lingard = NaN
1528 / 2921 Pol Lirola = $8.80m
1529 / 2921 Valentino Livramento = NaN
1530 / 2921 Dejan Ljubicic = $8.25m
1531 / 2921 Javier Llabrés = -
1532 / 2921 Diego Llorente = $19.80m
1533 / 2921 Marcos Llorente = $49.50m
1534 / 2921 Hugo Lloris = $7.70m
1535 / 2921 Giovani Lo Celso = NaN
1536 / 2921 Giovani Lo Celso = $24.20m
1537 / 2921 Julen Lobete = $5.50m
1538 / 2921 Stanislav Lobotka = $22.00m
1539 / 2921 Jürgen Locadia = NaN
1540 / 2921 Jürgen Locadia = $2.20m
1541 / 2921 Manuel Locatelli = $38.50m
1542 / 2921 Banzouzi Locko = $2.20m
1543 / 2921 Renan Lodi = $33.00m
1544 / 2921 Ruben Loftus-Cheek = $22.00m
1545 / 2921 Kevin Long = $1.10m
1546 / 2921 Shane Long = $880Th.
1547 / 2921 Sean Longstaff = NaN
1548 / 2921 Ademola Lookman = $11.00m
1549 / 2921 Anthony Lopes = $11.00m
1550 / 2921 David López = $550Th.
1551 / 2921 Diego López = $550Th.
1552 / 2921 Javi López = $770Th.
1553 / 2921 Maxime Lopez = $16.50m
1554 / 2921 Pau López = $13.20m
1555 / 2921 Unai López = $4.40m
1556 / 2921 Dion Lopy = $2.75m
1557 / 2921 Samuel Loric = $660Th.
1558 / 2921 Anthony Losilla = $550Th.
1559 / 2921 Jonas Lössl = $1.10m
1560 / 2921 Marcel Lotka = $1.43m
1561 / 2921 Jordan Lotomba = $7.70m
1562 / 2921 Johaneko Louisjean = $330Th.
1563 / 2921 Imran Louza = $9.90m
1564 / 2921 Matteo Lovato = NaN
1565 / 2921 Matteo Lovato = $7.70m
1566 / 2921 Eduard Löwen = $2.20m
1567 / 2921 Matthew Lowton = $1.32m
1568 / 2921 Anthony Lozano = NaN
1569 / 2921 Hirving Lozano = $33.00m
1570 / 2921 Jean Lucas = $8.80m
1571 / 2921 Anderson Lucoqui = $1.98m
1572 / 2921 José Luis Gayà = NaN
1573 / 2921 José Luis Morales = NaN
1574 / 2921 José Luis Palomino = NaN
1575 / 2921 Florentino Luís = NaN
1576 / 2921 Luismi = NaN
1577 / 2921 Douglas Luiz = $41.80m
1578 / 2921 Romelu Lukaku = $77.00m
1579 / 2921 Castello Lukeba = $22.00m
1580 / 2921 Dodi Lukebakio = NaN
1581 / 2921 Dodi Lukebakio = $8.25m
1582 / 2921 Saša Lukić = $16.50m
1583 / 2921 Levi Lumeka = NaN
1584 / 2921 Andriy Lunin = $2.75m
1585 / 2921 Andrey Lunyov = NaN
1586 / 2921 Sebastiano Luperto = $3.30m
1587 / 2921 Andreas Luthe = $880Th.
1588 / 2921 Lyanco = NaN
1589 / 2921 Charalambos Lykogiannis = $2.42m
1590 / 2921 Yannis M'Bemba = $220Th.
1591 / 2921 Alexis Mac Allister = $17.60m
1592 / 2921 Deiver Machado = $1.98m
1593 / 2921 Darwin Machís = $6.60m
1594 / 2921 Yvann Macon = $4.95m
1595 / 2921 James Maddison = $55.00m
1596 / 2921 Niki Mäenpää = $220Th.
1597 / 2921 Pablo Maffeo = $5.50m
1598 / 2921 Louis Mafouta = $990Th.
1599 / 2921 Giulio Maggiore = $9.35m
1600 / 2921 Matej Maglica = NaN
1601 / 2921 Francesco Magnanelli = $440Th.
1602 / 2921 Giangiacomo Magnani = NaN
1603 / 2921 Giangiacomo Magnani = $3.96m
1604 / 2921 Hugo Magnetti = $2.20m
1605 / 2921 Yohann Magnin = $2.20m
1606 / 2921 Harry Maguire = NaN
1607 / 2921 Riyad Mahrez = $38.50m
1608 / 2921 Arne Maier = $6.05m
1609 / 2921 Habib Maïga = $3.85m
1610 / 2921 Mike Maignan = $38.50m
1611 / 2921 Ainsley Maitland-Niles = NaN
1612 / 2921 Ainsley Maitland-Niles = $11.00m
1613 / 2921 Josh Maja = NaN
1614 / 2921 Abdul Majeed Waris = NaN
1615 / 2921 Lovro Majer = $22.00m
1616 / 2921 Jean-Victor Makengo = $8.25m
1617 / 2921 Béni Makouana = $880Th.
1618 / 2921 Nemanja Maksimović = $15.40m
1619 / 2921 Nikola Maksimović = $2.75m
1620 / 2921 Kévin Malcuit = $1.10m
1621 / 2921 Daniel Maldini = $2.20m
1622 / 2921 Youssef Maleh = $5.50m
1623 / 2921 Donyell Malen = $29.70m
1624 / 2921 Ruslan Malinovskyi = $30.80m
1625 / 2921 Hugo Mallo = $4.40m
1626 / 2921 Lennard Maloney = $385Th.
1627 / 2921 Mickael Malsa = $3.30m
1628 / 2921 Giorgi Mamardashvili = $11.00m
1629 / 2921 Rey Manaj = $1.65m
1630 / 2921 Hianga Mananga Mbock = NaN
1631 / 2921 Vincent Manceau = $880Th.
1632 / 2921 Gianluca Mancini = $23.10m
1633 / 2921 Leonardo Mancuso = NaN
1634 / 2921 Steve Mandanda = $1.65m
1635 / 2921 Reinildo Mandava = $24.20m
1636 / 2921 Reinildo Mandava = NaN
1637 / 2921 Aïssa Mandi = $6.60m
1638 / 2921 Rolando Mandragora = $11.00m
1639 / 2921 Anthony Mandrea = $1.10m
1640 / 2921 Sadio Mané = $77.00m
1641 / 2921 Eliaquim Mangala = $1.98m
1642 / 2921 Orel Mangala = $16.50m
1643 / 2921 Thomas Mangani = $660Th.
1644 / 2921 Ricardo Mangas = $1.65m
1645 / 2921 Kostas Manolas = NaN
1646 / 2921 Javier Manquillo = NaN
1647 / 2921 Myziane Maolida = NaN
1648 / 2921 Myziane Maolida = $3.30m
1649 / 2921 Sekou Mara = $3.30m
1650 / 2921 Maranhão = NaN
1651 / 2921 Nikola Maraš = $1.32m
1652 / 2921 Fernando Marçal = NaN
1653 / 2921 Marcelo = NaN
1654 / 2921 Marcelo = NaN
1655 / 2921 Marcelo = NaN
1656 / 2921 Solly March = $9.90m
1657 / 2921 Riccardo Marchizza = $2.20m
1658 / 2921 Gian Marco Ferrari = NaN
1659 / 2921 Iván Marcone = $2.20m
1660 / 2921 Óscar de Marcos = NaN
1661 / 2921 Davide Marfella = $55Th.
1662 / 2921 José Mari = $440Th.
1663 / 2921 Pablo Marí = NaN
1664 / 2921 Pablo Marí = $5.50m
1665 / 2921 José María Giménez = NaN
1666 / 2921 Mariano = NaN
1667 / 2921 Răzvan Marin = $13.20m
1668 / 2921 Guillermo Maripán = $16.50m
1669 / 2921 Mika Màrmol = $880Th.
1670 / 2921 Omar Marmoush = NaN
1671 / 2921 Omar Marmoush = $6.60m
1672 / 2921 Marquinhos = NaN
1673 / 2921 Roger Martí = $5.50m
1674 / 2921 Anthony Martial = NaN
1675 / 2921 Anthony Martial = $17.60m
1676 / 2921 Martín = NaN
1677 / 2921 Aarón Martín = $5.50m
1678 / 2921 Ander Martin = $330Th.
1679 / 2921 Andrés Martín = NaN
1680 / 2921 Carlos Martín = NaN
1681 / 2921 Iván Martín = NaN
1682 / 2921 Jonas Martin = $2.75m
1683 / 2921 Martinelli = NaN
1684 / 2921 Lucas Martínez Quarta = $13.20m
1685 / 2921 Emiliano Martínez = $30.80m
1686 / 2921 Iñigo Martínez = NaN
1687 / 2921 Javi Martínez = $2.75m
1688 / 2921 Josep Martinez = $2.42m
1689 / 2921 Lautaro Martínez = $82.50m
1690 / 2921 Pablo Martínez = NaN
1691 / 2921 Gelson Martins = $16.50m
1692 / 2921 Adam Marušić = $8.25m
1693 / 2921 Omar Mascarell = $4.40m
1694 / 2921 Andrea Masiello = $440Th.
1695 / 2921 Adam Masina = $3.30m
1696 / 2921 Erhan Mašović = $2.75m
1697 / 2921 Roberto Massimo = $2.20m
1698 / 2921 Arthur Masuaku = $5.50m
1699 / 2921 Jaime Mata = $3.30m
1700 / 2921 Juan Mata = NaN
1701 / 2921 Pape Matar Sarr = NaN
1702 / 2921 Eliot Matazo = $8.80m
1703 / 2921 Aleš Matějů = $1.10m
1704 / 2921 Jean-Philippe Mateta = $11.00m
1705 / 2921 Nemanja Matić = NaN
1706 / 2921 Joël Matip = $19.80m
1707 / 2921 Chrislain Matsima = $5.50m
1708 / 2921 Azor Matusiwa = $7.70m
1709 / 2921 Neal Maupay = $19.80m
1710 / 2921 Stephy Mavididi = $11.00m
1711 / 2921 Konstantinos Mavropanos = $16.50m
1712 / 2921 Eric Maxim Choupo-Moting = NaN
1713 / 2921 Luís Maximiano = $13.20m
1714 / 2921 Borja Mayoral = $9.90m
1715 / 2921 Borja Mayoral = NaN
1716 / 2921 Derek Mazou-Sacko = $275Th.
1717 / 2921 Pasquale Mazzocchi = NaN
1718 / 2921 Pasquale Mazzocchi = $1.65m
1719 / 2921 Kevin Mbabu = $9.90m
1720 / 2921 Kylian Mbappé = NaN
1721 / 2921 Ibrahima Mbaye = $770Th.
1722 / 2921 Amadou Mbengue = $880Th.
1723 / 2921 Bryan Mbeumo = $24.20m
1724 / 2921 Jason Mbock = $220Th.
1725 / 2921 Jordi Mboula = NaN
1726 / 2921 Moustapha Mbow = NaN
1727 / 2921 Nathanael Mbuku = $8.80m
1728 / 2921 James McArthur = $1.32m
1729 / 2921 James Mcatee = $3.30m
1730 / 2921 Kasey McAteer = $330Th.
1731 / 2921 Liam McCarron = $550Th.
1732 / 2921 Alex McCarthy = $3.30m
1733 / 2921 John McGinn = $35.20m
1734 / 2921 Weston McKennie = $27.50m
1735 / 2921 Stuart McKinstry = $220Th.
1736 / 2921 Kenny McLean = $2.75m
1737 / 2921 Dwight McNeil = $19.80m
1738 / 2921 Scott McTominay = NaN
1739 / 2921 Gary Medel = $1.10m
1740 / 2921 Facundo Medina = $12.10m
1741 / 2921 Nathan de Medina = $825Th.
1742 / 2921 Ben Mee = $2.75m
1743 / 2921 Admir Mehmedi = NaN
1744 / 2921 Hannibal Mejbri = NaN
1745 / 2921 Farid El Melali = NaN
1746 / 2921 Filippo Melegoni = $2.20m
1747 / 2921 Óscar Melendo = $3.30m
1748 / 2921 Gonzalo Melero = $4.40m
1749 / 2921 Birger Meling = $7.70m
1750 / 2921 Arthur Melo = NaN
1751 / 2921 Houboulang Mendes = $3.30m
1752 / 2921 Nuno Mendes = NaN
1753 / 2921 Pedro Mendes = $4.40m
1754 / 2921 Thiago Mendes = $9.90m
1755 / 2921 Tomás Mendes = -
1756 / 2921 Brais Méndez = $19.80m
1757 / 2921 Édgar Méndez = $1.32m
1758 / 2921 Arial Mendy = $770Th.
1759 / 2921 Batista Mendy = $3.30m
1760 / 2921 Benjamin Mendy = -
1761 / 2921 Edouard Mendy = $35.20m
1762 / 2921 Ferland Mendy = $44.00m
1763 / 2921 Nampalys Mendy = $4.40m
1764 / 2921 Gideon Mensah = $2.75m
1765 / 2921 Jorge Meré = NaN
1766 / 2921 Alex Meret = $11.00m
1767 / 2921 Fran Mérida = $990Th.
1768 / 2921 Mikel Merino = $55.00m
1769 / 2921 Quentin Merlin = $5.50m
1770 / 2921 Martín Merquelanz = $1.65m
1771 / 2921 Yıldırım Mert Çetin = NaN
1772 / 2921 Dries Mertens = $4.40m
1773 / 2921 Illan Meslier = $22.00m
1774 / 2921 Lionel Messi = NaN
1775 / 2921 Junior Messias = $6.60m
1776 / 2921 Thomas Meunier = $7.15m
1777 / 2921 Mexer = NaN
1778 / 2921 Marco Meyerhöfer = $2.20m
1779 / 2921 Matt Miazga = $2.20m
1780 / 2921 Kingsley Michael = $440Th.
1781 / 2921 Sven Michel = $1.32m
1782 / 2921 Edouard Michut = NaN
1783 / 2921 Miguel = NaN
1784 / 2921 Valentin Mihaila = $6.05m
1785 / 2921 Mikael = NaN
1786 / 2921 Hilmir Mikaelsson = -
1787 / 2921 Georges Mikautadze = NaN
1788 / 2921 William Mikelbrencis = $2.20m
1789 / 2921 Nikola Milenković = $22.00m
1790 / 2921 Arkadiusz Milik = $17.60m
1791 / 2921 Sergej Milinković-Savić = $77.00m
1792 / 2921 Vanja Milinković-Savić = $2.75m
1793 / 2921 Éder Militão = $66.00m
1794 / 2921 Luka Milivojević = $4.40m
1795 / 2921 Luis Milla = $5.50m
1796 / 2921 Pere Milla = $3.30m
1797 / 2921 Enzo Millot = $2.20m
1798 / 2921 James Milner = $2.20m
1799 / 2921 Santi Mina = -
1800 / 2921 Yerry Mina = $22.00m
1801 / 2921 Takumi Minamino = $13.20m
1802 / 2921 Tyrone Mings = $33.00m
1803 / 2921 Óscar Mingueza = $6.60m
1804 / 2921 Rafa Mir = $17.60m
1805 / 2921 Jorge Miramón = $1.98m
1806 / 2921 Aleksei Miranchuk = $11.00m
1807 / 2921 Juan Miranda = $5.50m
1808 / 2921 Fabio Miretti = $5.50m
1809 / 2921 Tyrick Mitchell = $27.50m
1810 / 2921 Stefan Mitrović = $1.98m
1811 / 2921 Maximilian Mittelstädt = $4.95m
1812 / 2921 Henrikh Mkhitaryan = $8.80m
1813 / 2921 Jakub Moder = $13.20m
1814 / 2921 Anthony Modeste = $3.30m
1815 / 2921 Marco Modolo = $440Th.
1816 / 2921 Luka Modrić = $11.00m
1817 / 2921 Terem Moffi = $9.90m
1818 / 2921 Kevin Möhwald = $1.65m
1819 / 2921 Dion Moise Sahi = NaN
1820 / 2921 Johan Mojica = $5.50m
1821 / 2921 Clinton Mola = $1.10m
1822 / 2921 Jorge Molina = $1.65m
1823 / 2921 Nahuel Molina = $22.00m
1824 / 2921 Cristian Molinaro = $275Th.
1825 / 2921 Florent Mollet = $6.60m
1826 / 2921 Jon Moncayola = $13.20m
1827 / 2921 Monchu = NaN
1828 / 2921 Thomas Monconduit = $1.65m
1829 / 2921 Gonzalo Montiel = $15.40m
1830 / 2921 Lorenzo Montipò = $4.95m
1831 / 2921 Ángel Montoro = $1.10m
1832 / 2921 Martín Montoya = $1.65m
1833 / 2921 Wesley Moraes = NaN
1834 / 2921 Álvaro Morata = $27.50m
1835 / 2921 Jan Morávek = $550Th.
1836 / 2921 Jon Morcillo = NaN
1837 / 2921 Jérémy Morel = $330Th.
1838 / 2921 Alberto Moreno = $5.50m
1839 / 2921 Álex Moreno = $19.80m
1840 / 2921 Gerard Moreno = $44.00m
1841 / 2921 Tete Morente = NaN
1842 / 2921 Ilaix Moriba = $9.90m
1843 / 2921 Ilaix Moriba = NaN
1844 / 2921 Manu Morlanes = $5.50m
1845 / 2921 Raúl Moro = $3.08m
1846 / 2921 Loren Morón = $4.40m
1847 / 2921 Loren Morón = NaN
1848 / 2921 Tyler Morton = $3.30m
1849 / 2921 Cristhian Mosquera = $1.10m
1850 / 2921 Lebo Mothiba = $2.20m
1851 / 2921 Lebo Mothiba = NaN
1852 / 2921 Andrei Motoc = $28Th.
1853 / 2921 Baptiste Mouazan = $220Th.
1854 / 2921 Aimen Moueffek = $1.65m
1855 / 2921 Youssoufa Moukoko = $16.50m
1856 / 2921 Harold Moukoudi = $4.40m
1857 / 2921 Jessy Moulin = $275Th.
1858 / 2921 Steve Mounié = $7.70m
1859 / 2921 Mason Mount = $82.50m
1860 / 2921 Lucas Moura = $22.00m
1861 / 2921 Lys Mousset = $6.60m
1862 / 2921 João Moutinho = NaN
1863 / 2921 Samuel Moutoussamy = $3.30m
1864 / 2921 Toni Moya = $1.65m
1865 / 2921 Samuel Mráz = NaN
1866 / 2921 Nordi Mukiele = $22.00m
1867 / 2921 Mert Müldür = $7.70m
1868 / 2921 Florian Müller = $6.05m
1869 / 2921 Thomas Müller = $24.20m
1870 / 2921 Bali Mumba = $440Th.
1871 / 2921 Marshall Munetsi = $5.50m
1872 / 2921 Iker Muniain = NaN
1873 / 2921 Aihen Muñoz = $6.60m
1874 / 2921 Luis Muriel = $22.00m
1875 / 2921 Jeison Murillo = $3.85m
1876 / 2921 Vedat Muriqi = $7.70m
1877 / 2921 Vedat Muriqi = NaN
1878 / 2921 Jacob Murphy = NaN
1879 / 2921 Nicola Murru = $2.20m
1880 / 2921 Yunus Musah = $16.50m
1881 / 2921 Torben Müsel = NaN
1882 / 2921 Jamal Musiala = $71.50m
1883 / 2921 Juan Musso = $19.80m
1884 / 2921 Shkodran Mustafi = $2.20m
1885 / 2921 Junior Mwanga = $220Th.
1886 / 2921 Enock Mwepu = $19.80m
1887 / 2921 Vitaliy Mykolenko = $19.80m
1888 / 2921 Joakim Mæhle = NaN
1889 / 2921 Obite N'Dicka = NaN
1890 / 2921 Kévin N'Doram = $2.20m
1891 / 2921 Eric N'Jo = $440Th.
1892 / 2921 Vital N'Simba = NaN
1893 / 2921 Nacho = NaN
1894 / 2921 Mickael Nade = $2.75m
1895 / 2921 Marvelous Nakamba = $7.70m
1896 / 2921 Nahitan Nández = $14.30m
1897 / 2921 Nani = NaN
1898 / 2921 Paul Nardi = $2.20m
1899 / 2921 Nikolas Nartey = $1.98m
1900 / 2921 Matija Nastasić = $1.65m
1901 / 2921 Roberto Navarro = $3.30m
1902 / 2921 Ximo Navarro = $1.32m
1903 / 2921 Jesús Navas = $3.30m
1904 / 2921 Keylor Navas = NaN
1905 / 2921 Papa Ndiaga Yade = NaN
1906 / 2921 Loum Ndiaye = NaN
1907 / 2921 Wilfred Ndidi = $66.00m
1908 / 2921 Tanguy Ndombele = NaN
1909 / 2921 Tanguy Ndombele = $33.00m
1910 / 2921 Dan Ndoye = NaN
1911 / 2921 Paul Nebel = $1.43m
1912 / 2921 Álvaro Negredo = $1.65m
1913 / 2921 Reiss Nelson = NaN
1914 / 2921 David Nemeth = $2.53m
1915 / 2921 Ilija Nestorovski = $770Th.
1916 / 2921 Neto = NaN
1917 / 2921 Pedro Neto = NaN
1918 / 2921 Luca Netz = NaN
1919 / 2921 Manuel Neuer = $16.50m
1920 / 2921 Florian Neuhaus = NaN
1921 / 2921 Carlos Neva = $4.40m
1922 / 2921 Rúben Neves = NaN
1923 / 2921 Neymar = NaN
1924 / 2921 Yvan Neyou = $3.30m
1925 / 2921 Jeremy Ngakia = $3.85m
1926 / 2921 Jessic Ngankam = $2.20m
1927 / 2921 Opa Nguette = $2.20m
1928 / 2921 Aurélien Nguiamba = $440Th.
1929 / 2921 Sikou Niakaté = $1.98m
1930 / 2921 Moussa Niakhate = $19.80m
1931 / 2921 Ibrahima Niane = $5.50m
1932 / 2921 M'Baye Niang = $4.40m
1933 / 2921 Cheikh Niasse = NaN
1934 / 2921 Florian Niederlechner = $2.42m
1935 / 2921 Håvard Nielsen = $1.10m
1936 / 2921 Saúl Ñíguez = $27.50m
1937 / 2921 Saúl Ñíguez = NaN
1938 / 2921 Dimitris Nikolaou = $4.40m
1939 / 2921 Joakim Nilsson = $3.85m
1940 / 2921 Casimir Ninga = $1.32m
1941 / 2921 Fernando Niño = $3.30m
1942 / 2921 Eddie Nketiah = $17.60m
1943 / 2921 Nicolas Nkoulou = NaN
1944 / 2921 Christopher Nkunku = $88.00m
1945 / 2921 Lukas Nmecha = $19.80m
1946 / 2921 Mark Noble = $1.10m
1947 / 2921 Peru Nolaskoain = NaN
1948 / 2921 Nolito = NaN
1949 / 2921 Arnaud Nordin = $4.40m
1950 / 2921 Håvard Nordtveit = $1.10m
1951 / 2921 Mathias Normann = $11.00m
1952 / 2921 Connor Noß = NaN
1953 / 2921 Hugo Novoa = $3.30m
1954 / 2921 Soma Novothny = NaN
1955 / 2921 Jean-Pierre Nsame = $4.40m
1956 / 2921 Randy Ntekja = NaN
1957 / 2921 Alexander Nübel = $8.80m
1958 / 2921 Kasim Nuhu = NaN
1959 / 2921 Unai Núñez = NaN
1960 / 2921 Hans Nunoo Sarpei = NaN
1961 / 2921 Bram Nuytinck = $3.85m
1962 / 2921 Simeon Nwankwo = NaN
1963 / 2921 Gerzino Nyamsi = $8.25m
1964 / 2921 Allan Nyom = NaN
1965 / 2921 M'Bala Nzola = $2.20m
1966 / 2921 Christian Nørgaard = $15.40m
1967 / 2921 Adam Obert = $990Th.
1968 / 2921 Joel Obi = $550Th.
1969 / 2921 Jan Oblak = $49.50m
1970 / 2921 Lucas Ocampos = $27.50m
1971 / 2921 Bastian Oczipka = $660Th.
1972 / 2921 Brain Oddei = $990Th.
1973 / 2921 Álvaro Odriozola = $13.20m
1974 / 2921 Angelo Ogbonna = $1.32m
1975 / 2921 Florent Ogier = $770Th.
1976 / 2921 Stefano Okaka = NaN
1977 / 2921 David Okereke = $4.95m
1978 / 2921 Masaya Okugawa = $5.50m
1979 / 2921 Lucas Olaza = $2.75m
1980 / 2921 Oier Olazábal = $330Th.
1981 / 2921 Mathias Olesen = $385Th.
1982 / 2921 Michael Olise = $24.20m
1983 / 2921 Christian Oliva = NaN
1984 / 2921 Brian Oliván = $2.75m
1985 / 2921 Sérgio Oliveira = $13.20m
1986 / 2921 Mathías Olivera = $16.50m
1987 / 2921 Dani Olmo = $44.00m
1988 / 2921 Robin Olsen = $2.20m
1989 / 2921 Warmed Omari = $8.80m
1990 / 2921 Jonas Omlin = $8.80m
1991 / 2921 Andrew Omobamidele = $1.32m
1992 / 2921 Amadou Onana = $11.00m
1993 / 2921 Jean Onana = $5.50m
1994 / 2921 Karim Onisiwo = $3.85m
1995 / 2921 Javier Ontiveros = NaN
1996 / 2921 Tyler Onyango = $220Th.
1997 / 2921 Frank Onyeka = $9.90m
1998 / 2921 Willi Orban = $11.00m
1999 / 2921 Divock Origi = $13.20m
2000 / 2921 Riccardo Orsolini = $11.00m
2001 / 2921 Stefan Ortega = $6.60m
2002 / 2921 Pedro Ortiz = $165Th.
2003 / 2921 Victor Osimhen = $71.50m
2004 / 2921 Milutin Osmajić = NaN
2005 / 2921 David Ospina = $5.50m
2006 / 2921 Patrick Osterhage = $990Th.
2007 / 2921 Tomáš Ostrák = $825Th.
2008 / 2921 Otávio = NaN
2009 / 2921 Paulo Otávio = $6.60m
2010 / 2921 Dango Ouattara = $990Th.
2011 / 2921 Remi Oudin = $5.50m
2012 / 2921 Alexandre Oukidja = $1.32m
2013 / 2921 Azzedine Ounahi = $3.30m
2014 / 2921 Adam Ounas = $7.70m
2015 / 2921 Jesús Owono = $220Th.
2016 / 2921 Reece Oxford = $13.20m
2017 / 2921 Alex Oxlade-Chamberlain = $17.60m
2018 / 2921 Mikel Oyarzabal = $66.00m
2019 / 2921 Ambroise Oyongo = $1.98m
2020 / 2921 Salih Özcan = $14.30m
2021 / 2921 Levin Öztunalı = $1.65m
2022 / 2921 Fernando Pacheco = $5.50m
2023 / 2921 Jon Pacheco = $1.10m
2024 / 2921 Gonçalo Paciência = NaN
2025 / 2921 Daniele Padelli = $440Th.
2026 / 2921 Gavi Paez = NaN
2027 / 2921 Simone Pafundi = -
2028 / 2921 Vincent Pajot = $1.10m
2029 / 2921 Exequiel Palacios = $16.50m
2030 / 2921 Helibelton Palacios = $1.32m
2031 / 2921 Isaac Palazón Camacho = NaN
2032 / 2921 Nicolas Pallois = $1.65m
2033 / 2921 Cole Palmer = $6.60m
2034 / 2921 Erik Palmer-Brown = $3.30m
2035 / 2921 Emerson Palmieri = NaN
2036 / 2921 Emerson Palmieri = NaN
2037 / 2921 Martin Palumbo = $770Th.
2038 / 2921 Goran Pandev = NaN
2039 / 2921 Ivor Pandur = $1.10m
2040 / 2921 Miloš Pantović = $2.42m
2041 / 2921 Antonios Papadopoulos = $385Th.
2042 / 2921 Matondo-Merveille Papela = $440Th.
2043 / 2921 Lucas Paquetá = $38.50m
2044 / 2921 Rubén Pardo = NaN
2045 / 2921 Kevin Paredes = $5.50m
2046 / 2921 Leandro Paredes = NaN
2047 / 2921 Daniel Parejo = $9.90m
2048 / 2921 Fabiano Parisi = $4.95m
2049 / 2921 Raúl Parra = $165Th.
2050 / 2921 Thomas Partey = $41.80m
2051 / 2921 Mario Pašalić = $30.80m
2052 / 2921 Felix Passlack = $1.65m
2053 / 2921 Javier Pastore = $990Th.
2054 / 2921 Patric = NaN
2055 / 2921 Rui Patrício = $6.60m
2056 / 2921 Paulinho = NaN
2057 / 2921 Gabriel Paulista = $7.70m
2058 / 2921 Benjamin Pavard = $33.00m
2059 / 2921 Strahinja Pavlović = NaN
2060 / 2921 Leonardo Pavoletti = $1.76m
2061 / 2921 Dimitri Payet = $5.50m
2062 / 2921 Mads Pedersen = $2.20m
2063 / 2921 Alfonso Pedraza = $19.80m
2064 / 2921 Pedri = NaN
2065 / 2921 Pedro = NaN
2066 / 2921 João Pedro = $15.40m
2067 / 2921 João Pedro = $13.20m
2068 / 2921 Pedrosa = NaN
2069 / 2921 Gianluca Pegolo = $110Th.
2070 / 2921 Peter Pekarík = $770Th.
2071 / 2921 Pietro Pellegri = $4.95m
2072 / 2921 Pietro Pellegri = NaN
2073 / 2921 Lorenzo Pellegrini = $49.50m
2074 / 2921 Luca Pellegrini = $9.90m
2075 / 2921 Facundo Pellistri = $2.75m
2076 / 2921 Federico Peluso = $330Th.
2077 / 2921 Timothee Pembele = $6.60m
2078 / 2921 Rubén Peña = $2.75m
2079 / 2921 Philipp Pentke = $330Th.
2080 / 2921 Nicolas Pépé = $27.50m
2081 / 2921 Ricardo Pepi = $9.90m
2082 / 2921 Alberto Perea = $1.10m
2083 / 2921 Victor Perea = NaN
2084 / 2921 Mathias Pereira Lage = $2.75m
2085 / 2921 Brayann Pereira = $660Th.
2086 / 2921 Danilo Pereira = NaN
2087 / 2921 Ricardo Pereira = $22.00m
2088 / 2921 Gastón Pereiro = $3.30m
2089 / 2921 Luan Peres Petroni = NaN
2090 / 2921 Dor Peretz = $1.54m
2091 / 2921 Roberto Pereyra = $7.15m
2092 / 2921 Ayoze Pérez = $11.00m
2093 / 2921 Carles Pérez = $6.60m
2094 / 2921 Iñigo Pérez = $660Th.
2095 / 2921 Juan Pérez = $660Th.
2096 / 2921 Kike Pérez = $1.65m
2097 / 2921 Lucas Pérez = NaN
2098 / 2921 Lucas Pérez = $2.75m
2099 / 2921 Nehuén Pérez = $6.60m
2100 / 2921 Mattia Perin = $4.40m
2101 / 2921 Ivan Perišić = $11.00m
2102 / 2921 Sonny Perkins = $1.32m
2103 / 2921 Diego Perotti = $550Th.
2104 / 2921 Romain Perraud = $8.80m
2105 / 2921 Lucas Perrin = $5.50m
2106 / 2921 Mario Perrone = -
2107 / 2921 Pavao Pervan = $660Th.
2108 / 2921 Matteo Pessina = $17.60m
2109 / 2921 Andrea Petagna = $13.20m
2110 / 2921 Nils Petersen = $1.65m
2111 / 2921 Danijel Petković = $1.10m
2112 / 2921 Léo Pétrot = $1.10m
2113 / 2921 Njegoš Petrović = $2.75m
2114 / 2921 Jens Petter Hauge = NaN
2115 / 2921 Alex Petxa = NaN
2116 / 2921 Germán Pezzella = $5.50m
2117 / 2921 Giuseppe Pezzella = $5.50m
2118 / 2921 Immanuel Pherai = $770Th.
2119 / 2921 Maximilian Philipp = $4.95m
2120 / 2921 Romain Philippoteaux = NaN
2121 / 2921 Kalvin Phillips = $55.00m
2122 / 2921 Jaden Philogene Bidace = NaN
2123 / 2921 Jérôme Phojo = $330Th.
2124 / 2921 Krzysztof Piątek = NaN
2125 / 2921 Krzysztof Piątek = $13.20m
2126 / 2921 Pablo Piatti = $1.10m
2127 / 2921 Cristiano Piccini = NaN
2128 / 2921 Roberto Piccoli = $4.40m
2129 / 2921 Roberto Piccoli = NaN
2130 / 2921 Jordan Pickford = $22.00m
2131 / 2921 Jérémy Pied = $880Th.
2132 / 2921 Amos Pieper = $5.50m
2133 / 2921 Ronaël Pierre-Gabriel = $5.50m
2134 / 2921 Erik Pieters = $990Th.
2135 / 2921 Tomás Pina Isla = NaN
2136 / 2921 Andrea Pinamonti = $22.00m
2137 / 2921 Orbelín Pineda = $4.40m
2138 / 2921 Ethan Pinnock = $13.20m
2139 / 2921 Yeremi Pino = $44.00m
2140 / 2921 Carlo Pinsoglio = $440Th.
2141 / 2921 Riccardo Pinzi = -
2142 / 2921 Gerard Piqué = $5.50m
2143 / 2921 Marko Pjaca = $3.85m
2144 / 2921 Marvin Plattenhardt = $2.20m
2145 / 2921 Alassane Pléa = NaN
2146 / 2921 Tommaso Pobega = $17.60m
2147 / 2921 Daniel Podence = NaN
2148 / 2921 Suf Podgoreanu = $550Th.
2149 / 2921 Paul Pogba = NaN
2150 / 2921 Joel Pohjanpalo = NaN
2151 / 2921 Matteo Politano = $22.00m
2152 / 2921 Julian Pollersbeck = $880Th.
2153 / 2921 Sebastian Polter = $2.42m
2154 / 2921 Ezequiel Ponce = $5.50m
2155 / 2921 Marin Pongračić = $6.60m
2156 / 2921 Pere Pons = $2.75m
2157 / 2921 Nick Pope = $16.50m
2158 / 2921 Manolo Portanova = $2.75m
2159 / 2921 Portu = NaN
2160 / 2921 Stefan Posch = $11.00m
2161 / 2921 Sergio Postigo = $770Th.
2162 / 2921 Yussuf Poulsen = $15.40m
2163 / 2921 Gaëtan Poussin = $1.10m
2164 / 2921 Darío Poveda = NaN
2165 / 2921 José Pozo = NaN
2166 / 2921 Dennis Praet = $14.30m
2167 / 2921 Mateusz Praszelik = $1.54m
2168 / 2921 Abdón Prats = $1.65m
2169 / 2921 Sanjin Prcić = $4.40m
2170 / 2921 Nicolas de Préville = $2.75m
2171 / 2921 Isaac Price = -
2172 / 2921 Manuel Prietl = $1.98m
2173 / 2921 Alejandro Primo = -
2174 / 2921 Grischa Prömel = $8.25m
2175 / 2921 Nick Proschwitz = $165Th.
2176 / 2921 Ivan Provedel = $3.85m
2177 / 2921 Javi Puado = $8.80m
2178 / 2921 Pubill = -
2179 / 2921 Antonio Puertas = $4.95m
2180 / 2921 Riqui Puig = $7.70m
2181 / 2921 Teemu Pukki = $4.40m
2182 / 2921 Erick Pulgar = NaN
2183 / 2921 Christian Pulisic = $46.20m
2184 / 2921 Afimico Pululu = $1.10m
2185 / 2921 Ignacio Pussetto = $3.30m
2186 / 2921 Niklas Pyyhtiä = $165Th.
2187 / 2921 Przemysław Płacheta = $1.98m
2188 / 2921 Yacine Qasmi = NaN
2189 / 2921 Fabio Quagliarella = $1.10m
2190 / 2921 Quini = NaN
2191 / 2921 Dani Raba = NaN
2192 / 2921 Dani Raba = $990Th.
2193 / 2921 Adrien Rabiot = $18.70m
2194 / 2921 Uroš Račić = $7.70m
2195 / 2921 Nemanja Radoja = $2.20m
2196 / 2921 Nemanja Radonjić = NaN
2197 / 2921 Ivan Radovanović = $660Th.
2198 / 2921 Ionuț Radu = $3.85m
2199 / 2921 Ștefan Radu = $660Th.
2200 / 2921 Boris Radunović = $440Th.
2201 / 2921 Sidney Raebiger = $825Th.
2202 / 2921 Rafinha = NaN
2203 / 2921 Rafinha = NaN
2204 / 2921 Antonio Raillo = $2.20m
2205 / 2921 Antonio Raimondo = -
2206 / 2921 Predrag Rajković = $9.90m
2207 / 2921 Jesuran Rak Sakyi = NaN
2208 / 2921 Ivan Rakitić = $7.70m
2209 / 2921 Diant Ramaj = NaN
2210 / 2921 Jonás Ramalho = $880Th.
2211 / 2921 Adil Rami = $440Th.
2212 / 2921 Sandro Ramírez = $3.85m
2213 / 2921 Guilherme Ramos = $2.20m
2214 / 2921 Sergio Ramos = NaN
2215 / 2921 Aaron Ramsdale = $30.80m
2216 / 2921 Aaron Ramsey = $3.30m
2217 / 2921 Jacob Ramsey = $27.50m
2218 / 2921 Luca Ranieri = $2.75m
2219 / 2921 Andrea Ranocchia = $880Th.
2220 / 2921 Tobias Raschl = $1.10m
2221 / 2921 Elba Rashani = $2.75m
2222 / 2921 Marcus Rashford = NaN
2223 / 2921 Milot Rashica = $7.70m
2224 / 2921 Giacomo Raspadori = $30.80m
2225 / 2921 Raúl = NaN
2226 / 2921 David Raum = $22.00m
2227 / 2921 Nicola Ravaglia = $220Th.
2228 / 2921 David Raya = $24.20m
2229 / 2921 Ante Rebić = $18.70m
2230 / 2921 Arkadiusz Reca = $3.30m
2231 / 2921 Lluís Recasens = $275Th.
2232 / 2921 Nathan Redmond = $19.80m
2233 / 2921 Sergio Reguilón = $27.50m
2234 / 2921 Manolo Reina = $220Th.
2235 / 2921 Pepe Reina = $770Th.
2236 / 2921 Jeff Reine-Adélaïde = $15.40m
2237 / 2921 Reinier = NaN
2238 / 2921 Karim Rekik = $5.50m
2239 / 2921 Álex Remiro = $24.20m
2240 / 2921 Panagiotis Retsos = NaN
2241 / 2921 Panagiotis Retsos = $3.08m
2242 / 2921 Marco Reus = $12.10m
2243 / 2921 Elvis Rexhbeçaj = $4.40m
2244 / 2921 Gio Reyna = $38.50m
2245 / 2921 Bryan Reynolds = NaN
2246 / 2921 Nico Ribaudo = NaN
2247 / 2921 Tiago Ribeiro = $275Th.
2248 / 2921 Franck Ribéry = $1.10m
2249 / 2921 Samuele Ricci = $13.20m
2250 / 2921 Samuele Ricci = NaN
2251 / 2921 Declan Rice = $88.00m
2252 / 2921 Chris Richards = $8.25m
2253 / 2921 Chris Richards = NaN
2254 / 2921 Omar Richards = $7.70m
2255 / 2921 Taylor Richards = $440Th.
2256 / 2921 Richarlison = NaN
2257 / 2921 Marco Richter = $6.60m
2258 / 2921 Diego Rico = $3.30m
2259 / 2921 Sergio Rico = $4.40m
2260 / 2921 Sergio Rico = NaN
2261 / 2921 Jaïro Riedewald = $5.50m
2262 / 2921 Manuel Riemann = $1.32m
2263 / 2921 Tomás Rincón = NaN
2264 / 2921 Tomás Rincón = $1.10m
2265 / 2921 Luis Rioja = $3.30m
2266 / 2921 Renaud Ripart = $4.40m
2267 / 2921 Mihailo Ristić = $3.85m
2268 / 2921 Matt Ritchie = NaN
2269 / 2921 Maxence Rivera = $660Th.
2270 / 2921 Rober = NaN
2271 / 2921 Sergi Roberto = $6.60m
2272 / 2921 Connor Roberts = $2.75m
2273 / 2921 Patrick Roberts = NaN
2274 / 2921 Tyler Roberts = $8.80m
2275 / 2921 Andrew Robertson = $71.50m
2276 / 2921 Marc Roca = $7.70m
2277 / 2921 Rubén Rochina = $2.75m
2278 / 2921 Cimo Röcker = $110Th.
2279 / 2921 Enzo Roco = $2.20m
2280 / 2921 Stian Rode Gregersen = NaN
2281 / 2921 Sebastian Rode = NaN
2282 / 2921 Joe Rodon = $8.80m
2283 / 2921 Rodri = NaN
2284 / 2921 Rodrigo = NaN
2285 / 2921 Rodrigo = NaN
2286 / 2921 Gerson Rodrigues = NaN
2287 / 2921 Kévin Rodrigues = $1.98m
2288 / 2921 Óscar Rodríguez Arnaiz = NaN
2289 / 2921 Óscar Rodríguez Arnaiz = NaN
2290 / 2921 Ángel Rodríguez = $2.20m
2291 / 2921 Dani Rodríguez = $1.32m
2292 / 2921 Guido Rodríguez = $27.50m
2293 / 2921 Jay Rodriguez = $2.20m
2294 / 2921 Ricardo Rodríguez = $3.74m
2295 / 2921 Rodrygo = NaN
2296 / 2921 Mads Roerslev = $2.75m
2297 / 2921 Marko Rog = $6.60m
2298 / 2921 Rogério = NaN
2299 / 2921 Alessio Romagnoli = $18.70m
2300 / 2921 Simone Romagnoli = $1.10m
2301 / 2921 Leonardo Román = $330Th.
2302 / 2921 Cristian Romero = $52.80m
2303 / 2921 Iván Romero = $880Th.
2304 / 2921 Luka Romero = $30.80m
2305 / 2921 Sergio Romero = $770Th.
2306 / 2921 Oriol Romeu = $5.50m
2307 / 2921 Cristiano Ronaldo = NaN
2308 / 2921 Cristiano Ronaldo = NaN
2309 / 2921 Salomón Rondón = $2.75m
2310 / 2921 Valentin Rongier = $13.20m
2311 / 2921 Marten de Roon = $18.70m
2312 / 2921 Unai Ropero = -
2313 / 2921 Pablo Rosario = $11.00m
2314 / 2921 Romario Rösch = $220Th.
2315 / 2921 Danny Rose = $2.42m
2316 / 2921 Francesco Rossi = $110Th.
2317 / 2921 Tom Rothe = $1.32m
2318 / 2921 Jérôme Roussillon = $4.95m
2319 / 2921 Nicolò Rovella = $11.00m
2320 / 2921 Jon Rowe = $880Th.
2321 / 2921 Amir Rrahmani = $27.50m
2322 / 2921 Ruan = NaN
2323 / 2921 Rubén = NaN
2324 / 2921 John Ruddy = NaN
2325 / 2921 Antonio Rüdiger = $44.00m
2326 / 2921 Sebastian Rudy = $1.65m
2327 / 2921 Daniele Rugani = $3.85m
2328 / 2921 Matteo Ruggeri = $2.20m
2329 / 2921 Mário Rui = $8.80m
2330 / 2921 Aitor Ruibal = $3.30m
2331 / 2921 Fabián Ruiz Peña = NaN
2332 / 2921 Isma Ruiz = $660Th.
2333 / 2921 Víctor Ruiz = $1.65m
2334 / 2921 Gerónimo Rulli = $6.60m
2335 / 2921 Lukas Rupp = $1.32m
2336 / 2921 Alessandro Russo = -
2337 / 2921 Franco Russo = $1.32m
2338 / 2921 Georginio Rutter = $17.60m
2339 / 2921 Mathew Ryan = $5.50m
2340 / 2921 Julian Ryerson = $4.40m
2341 / 2921 Frederik Rønnow = $2.20m
2342 / 2921 José Sá = NaN
2343 / 2921 Roli Pereira de Sa = $770Th.
2344 / 2921 Youssouf Sabaly = $3.30m
2345 / 2921 Mathys Saban = $330Th.
2346 / 2921 Stefano Sabelli = NaN
2347 / 2921 Abdelhamid Sabiri = $3.30m
2348 / 2921 Marcel Sabitzer = $16.50m
2349 / 2921 Marcel Sabitzer = NaN
2350 / 2921 Falaye Sacko = $4.40m
2351 / 2921 Alexis Saelemaekers = $18.70m
2352 / 2921 Rafiki Said Ahamada = NaN
2353 / 2921 Wesley Saïd = $2.75m
2354 / 2921 Allan Saint-Maximin = NaN
2355 / 2921 Romain Saïss = NaN
2356 / 2921 Bukayo Saka = $71.50m
2357 / 2921 Mamadou Sakho = $4.95m
2358 / 2921 Bakary Sako = $440Th.
2359 / 2921 Jacopo Sala = $550Th.
2360 / 2921 Mohamed Salah = $99.00m
2361 / 2921 Eddie Salcedo = $3.85m
2362 / 2921 William Saliba = $33.00m
2363 / 2921 Romain Salin = $440Th.
2364 / 2921 Mohammed Salisu = $19.80m
2365 / 2921 Roland Sallai = $13.20m
2366 / 2921 Yoann Salmier = $2.20m
2367 / 2921 Salvi = NaN
2368 / 2921 Lazar Samardzic = $3.85m
2369 / 2921 Diadie Samassékou = $17.60m
2370 / 2921 Mama Samba Baldé = NaN
2371 / 2921 Albert Sambi Lokonga = NaN
2372 / 2921 Junior Sambia = $4.95m
2373 / 2921 Luigi Samele = $165Th.
2374 / 2921 Fede San Emeterio = $1.65m
2375 / 2921 Antonio Sanabria = $8.25m
2376 / 2921 Oihan Sancet = NaN
2377 / 2921 Renato Sanches = $30.80m
2378 / 2921 Alexis Sánchez = $3.85m
2379 / 2921 Antonio Sánchez = $1.65m
2380 / 2921 Davinson Sánchez = $30.80m
2381 / 2921 Germán Sánchez = $880Th.
2382 / 2921 Juan Sánchez = $1.65m
2383 / 2921 Manuel Sánchez = $11.00m
2384 / 2921 Robert Sánchez = $17.60m
2385 / 2921 Jadon Sancho = NaN
2386 / 2921 Philippe Sandler = NaN
2387 / 2921 Alex Sandro = $6.60m
2388 / 2921 Leroy Sané = $66.00m
2389 / 2921 Oier Sanjurjo = $550Th.
2390 / 2921 Mohamed Sankoh = $1.10m
2391 / 2921 Morgan Sanson = $11.00m
2392 / 2921 Nicola Sansone = $2.20m
2393 / 2921 Baptiste Santamaria = $15.40m
2394 / 2921 Baptiste Santamaria = NaN
2395 / 2921 Federico Santander = $660Th.
2396 / 2921 Jesús Santiago = NaN
2397 / 2921 Samir Santos = NaN
2398 / 2921 Samir Santos = NaN
2399 / 2921 Sergio Santos = $220Th.
2400 / 2921 Alvaro Sanz = $770Th.
2401 / 2921 Riccardo Saponara = $2.75m
2402 / 2921 Pablo Sarabia = NaN
2403 / 2921 Marcelo Saracchi = $2.75m
2404 / 2921 Josh Sargent = $7.70m
2405 / 2921 Jeremy Sarmiento = $3.30m
2406 / 2921 Bouna Sarr = $2.75m
2407 / 2921 Ismaila Sarr = $29.70m
2408 / 2921 Malang Sarr = $8.80m
2409 / 2921 Joan Sastre Vanrell = NaN
2410 / 2921 Giacomo Satalino = $220Th.
2411 / 2921 Martin Satriano = $7.70m
2412 / 2921 Martin Satriano = NaN
2413 / 2921 Téji Savanier = $13.20m
2414 / 2921 Esteban Saveljich = $880Th.
2415 / 2921 Stefan Savić = $17.60m
2416 / 2921 Joe Scally = NaN
2417 / 2921 Giorgio Scalvini = $11.00m
2418 / 2921 Gianluca Scamacca = $33.00m
2419 / 2921 Dane Scarlett = $275Th.
2420 / 2921 Kevin Schade = $7.70m
2421 / 2921 András Schäfer = $2.20m
2422 / 2921 Fabian Schär = NaN
2423 / 2921 Louis Schaub = $1.98m
2424 / 2921 Andrea Schiavone = $440Th.
2425 / 2921 Patrik Schick = $55.00m
2426 / 2921 Kingsley Schindler = $880Th.
2427 / 2921 Xaver Schlager = $29.70m
2428 / 2921 Keven Schlotterbeck = $3.30m
2429 / 2921 Nico Schlotterbeck = $36.30m
2430 / 2921 Jeffrey Schlupp = $8.80m
2431 / 2921 Kasper Schmeichel = $4.40m
2432 / 2921 Jonathan Schmid = $1.32m
2433 / 2921 Benno Schmitz = $3.30m
2434 / 2921 David Schnegg = NaN
2435 / 2921 Morgan Schneiderlin = $3.85m
2436 / 2921 Alessandro Schöpf = $2.75m
2437 / 2921 Jerdy Schouten = $9.90m
2438 / 2921 Nico Schulz = $4.40m
2439 / 2921 Marvin Schwäbe = $4.40m
2440 / 2921 Alexander Schwolow = $3.30m
2441 / 2921 Valentino Scotta = NaN
2442 / 2921 Demba Seck = $3.30m
2443 / 2921 Aleksandar Sedlar = $550Th.
2444 / 2921 Paul Seguin = $2.42m
2445 / 2921 Alidu Seidu = $2.20m
2446 / 2921 Davie Selke = $1.98m
2447 / 2921 Matz Sels = $8.80m
2448 / 2921 Ken Sema = $2.75m
2449 / 2921 Nélson Semedo = NaN
2450 / 2921 Lion Semić = $550Th.
2451 / 2921 Adrian Šemper = $1.65m
2452 / 2921 Stefano Sensi = $7.70m
2453 / 2921 Stefano Sensi = NaN
2454 / 2921 Luigi Sepe = $1.54m
2455 / 2921 Suat Serdar = $8.80m
2456 / 2921 Laurens Serpe = NaN
2457 / 2921 Janni Serra = $1.98m
2458 / 2921 Javier Serrano = NaN
2459 / 2921 Nicolás Serrano = NaN
2460 / 2921 Zidan Sertdemir = $2.20m
2461 / 2921 Ryan Sessegnon = $19.80m
2462 / 2921 Nils Seufert = NaN
2463 / 2921 Salva Sevilla = $330Th.
2464 / 2921 Stephan El Shaarawy = $6.60m
2465 / 2921 Jamie Shackleton = $2.75m
2466 / 2921 Xherdan Shaqiri = NaN
2467 / 2921 Luke Shaw = NaN
2468 / 2921 Jonjo Shelvey = NaN
2469 / 2921 Aimar Sher = $1.10m
2470 / 2921 Eldor Shomurodov = $8.80m
2471 / 2921 Shola Shoretire = NaN
2472 / 2921 Mahame Siby = NaN
2473 / 2921 Aboubakar Sidibé = NaN
2474 / 2921 Alassane Sidibe = $440Th.
2475 / 2921 Djibril Sidibé = $8.80m
2476 / 2921 Kaj Sierhuis = NaN
2477 / 2921 Francisco Sierralta = $1.65m
2478 / 2921 Arnór Sigurðsson = $2.53m
2479 / 2921 Kiliann Sildillia = $1.65m
2480 / 2921 Adrien Silva = NaN
2481 / 2921 André Silva = $35.20m
2482 / 2921 Bernardo Silva = $88.00m
2483 / 2921 Caio Henrique Oliveira Silva = NaN
2484 / 2921 David Silva = $4.40m
2485 / 2921 Fábio Silva = NaN
2486 / 2921 Gabriel Silva = $1.32m
2487 / 2921 Jonathan Silva = $1.98m
2488 / 2921 Rui Silva = $16.50m
2489 / 2921 Thiago Silva = $2.75m
2490 / 2921 Marco Silvestri = $5.50m
2491 / 2921 Mohamed Simakan = $24.20m
2492 / 2921 Giovanni Simeone = $18.70m
2493 / 2921 Giovanni Simeone = NaN
2494 / 2921 Giuliano Simeone = NaN
2495 / 2921 Ellis Simms = $330Th.
2496 / 2921 Moses Simon = $11.00m
2497 / 2921 Unai Simón = NaN
2498 / 2921 Xavi Simons = NaN
2499 / 2921 Wilfried Singo = NaN
2500 / 2921 Daley Sinkgraven = $4.40m
2501 / 2921 Tobias Sippel = NaN
2502 / 2921 Hugo Siquet = $6.05m
2503 / 2921 Salvatore Sirigu = $1.65m
2504 / 2921 Issouf Sissokho = $550Th.
2505 / 2921 Ibrahima Sissoko = $8.80m
2506 / 2921 Moussa Sissoko = $4.95m
2507 / 2921 Nelson Sissoko = NaN
2508 / 2921 Sambou Sissoko = $550Th.
2509 / 2921 Antonio Sivera = $1.10m
2510 / 2921 Ellyes Skhiri = $14.30m
2511 / 2921 Oliver Skipp = $19.80m
2512 / 2921 Leo Skiri Østigård = NaN
2513 / 2921 Łukasz Skorupski = $4.40m
2514 / 2921 Andreas Skov Olsen = NaN
2515 / 2921 Robert Skov = $7.70m
2516 / 2921 Milan Škriniar = $71.50m
2517 / 2921 Islam Slimani = NaN
2518 / 2921 William Smallbone = $2.20m
2519 / 2921 Chris Smalling = $8.80m
2520 / 2921 Emile Smith Rowe = $44.00m
2521 / 2921 Cédric Soares = $6.60m
2522 / 2921 Danilo Soares = $2.42m
2523 / 2921 Rubén Sobrino = NaN
2524 / 2921 Rubén Sobrino = $2.20m
2525 / 2921 Alex Sola = $330Th.
2526 / 2921 Augusto Solari = $2.75m
2527 / 2921 Roberto Soldado = $1.10m
2528 / 2921 Carlos Soler = $55.00m
2529 / 2921 Yann Sommer = NaN
2530 / 2921 Son = NaN
2531 / 2921 Brandon Soppy = $5.50m
2532 / 2921 David Soria = $8.80m
2533 / 2921 Roberto Soriano = $5.50m
2534 / 2921 Alberto Soro = $2.75m
2535 / 2921 Borna Sosa = $25.30m
2536 / 2921 Hugo Sotelo = -
2537 / 2921 Florian Sotoca = $2.75m
2538 / 2921 Riccardo Sottil = $9.90m
2539 / 2921 Tomáš Souček = $49.50m
2540 / 2921 Matìas Soulé = $2.75m
2541 / 2921 Sambou Soumano = $1.32m
2542 / 2921 Adama Soumaoro = $4.40m
2543 / 2921 Boubakary Soumaré = $27.50m
2544 / 2921 Arnaud Souquet = $2.75m
2545 / 2921 Djibril Sow = NaN
2546 / 2921 Saidou Sow = $4.40m
2547 / 2921 Çağlar Söyüncü = $44.00m
2548 / 2921 Leonardo Spinazzola = $22.00m
2549 / 2921 Marco Sportiello = $1.65m
2550 / 2921 Tony Springett = -
2551 / 2921 Jerry St. Juste = $17.60m
2552 / 2921 Anton Stach = $14.30m
2553 / 2921 Kostas Stafylidis = $1.98m
2554 / 2921 Luca Stanga = $165Th.
2555 / 2921 Josip Stanišić = $4.40m
2556 / 2921 Niklas Stark = $7.15m
2557 / 2921 Jason Steele = $660Th.
2558 / 2921 Jed Steer = $660Th.
2559 / 2921 Renato Steffen = $2.20m
2560 / 2921 Zack Steffen = $6.60m
2561 / 2921 Calvin Stengs = $14.30m
2562 / 2921 Pascal Stenzel = $1.65m
2563 / 2921 Dale Stephens = $1.10m
2564 / 2921 Jack Stephens = $7.70m
2565 / 2921 Raheem Sterling = $77.00m
2566 / 2921 Finley Stevens = $440Th.
2567 / 2921 Angelo Stiller = $7.70m
2568 / 2921 Lars Stindl = NaN
2569 / 2921 Riccardo Stivanello = -
2570 / 2921 Kevin Stöger = $1.76m
2571 / 2921 Petar Stojanović = $3.30m
2572 / 2921 John Stones = $30.80m
2573 / 2921 Thomas Strakosha = $7.70m
2574 / 2921 Stefan Strandberg = $550Th.
2575 / 2921 Dávid Strelec = $1.54m
2576 / 2921 Tobias Strobl = $990Th.
2577 / 2921 Kevin Strootman = $1.65m
2578 / 2921 Pascal Struijk = $16.50m
2579 / 2921 Jens Stryger Larsen = $2.20m
2580 / 2921 Leo Štulac = $2.42m
2581 / 2921 Stefano Sturaro = $3.30m
2582 / 2921 Damián Suárez = $1.65m
2583 / 2921 Denis Suárez = $11.00m
2584 / 2921 Luis Suárez = $8.80m
2585 / 2921 Mario Suárez = $330Th.
2586 / 2921 Isaac Success = $3.30m
2587 / 2921 Niklas Süle = $38.50m
2588 / 2921 Kamaldeen Sulemana = $19.80m
2589 / 2921 Crysencio Summerville = $2.20m
2590 / 2921 Vladyslav Supriaha = NaN
2591 / 2921 Suso = NaN
2592 / 2921 Bosko Sutalo = $3.30m
2593 / 2921 Mattias Svanberg = $16.50m
2594 / 2921 Michael Svoboda = $990Th.
2595 / 2921 Abdoulaye Sylla = $275Th.
2596 / 2921 Mamadou Sylla = NaN
2597 / 2921 Mamadou Sylla = $1.32m
2598 / 2921 Ádám Szalai = NaN
2599 / 2921 Wojciech Szczęsny = $16.50m
2600 / 2921 Dominik Szoboszlai = $30.80m
2601 / 2921 Jakob Sørensen = NaN
2602 / 2921 Alexander Sørloth = $11.00m
2603 / 2921 Tachi = NaN
2604 / 2921 Jonathan Tah = $27.50m
2605 / 2921 Waniss Taibi = $660Th.
2606 / 2921 Flavien Tait = $8.80m
2607 / 2921 Adrien Tameze = $11.00m
2608 / 2921 Japhet Tanganga = $13.20m
2609 / 2921 Renato Tapia = $11.00m
2610 / 2921 Edmond Tapsoba = $38.50m
2611 / 2921 Florian Tardieu = $3.30m
2612 / 2921 Matt Targett = $18.70m
2613 / 2921 Matt Targett = NaN
2614 / 2921 Oussama Targhalline = $220Th.
2615 / 2921 James Tarkowski = $24.20m
2616 / 2921 Ciprian Tătărușanu = $660Th.
2617 / 2921 Niklas Tauer = $1.65m
2618 / 2921 Nuno Tavares = $16.50m
2619 / 2921 Charlie Taylor = $3.30m
2620 / 2921 Loum Tchaouna = $2.20m
2621 / 2921 Warren Tchimbembé = NaN
2622 / 2921 Aurélien Tchouaméni = $66.00m
2623 / 2921 Mathys Tel = $2.20m
2624 / 2921 Jordan Tell = NaN
2625 / 2921 Nathan Tella = $1.98m
2626 / 2921 Alex Telles = NaN
2627 / 2921 Cristian Tello = $5.50m
2628 / 2921 Nahuel Tenaglia = $3.30m
2629 / 2921 Marc Tenas = $550Th.
2630 / 2921 Marc-André ter Stegen = $33.00m
2631 / 2921 Filippo Terracciano = -
2632 / 2921 Pietro Terracciano = $1.65m
2633 / 2921 Martin Terrier = $27.50m
2634 / 2921 Aleksa Terzić = $1.65m
2635 / 2921 Robert Tesche = $385Th.
2636 / 2921 Tanner Tessmann = $2.75m
2637 / 2921 Tetê = NaN
2638 / 2921 Cedric Teuchert = NaN
2639 / 2921 Arthur Theate = $16.50m
2640 / 2921 Jan Thielmann = $8.25m
2641 / 2921 Sada Thioub = NaN
2642 / 2921 Sada Thioub = $2.20m
2643 / 2921 Luke Thomas = $13.20m
2644 / 2921 Romain Thomas = $1.10m
2645 / 2921 Adrien Thomasson = $9.90m
2646 / 2921 Erik Thommy = $1.98m
2647 / 2921 Dominic Thompson = $880Th.
2648 / 2921 Morten Thorsby = $7.70m
2649 / 2921 Thuler = NaN
2650 / 2921 Marcus Thuram = NaN
2651 / 2921 Khéphren Thuram-Ulie = NaN
2652 / 2921 Alexis Tibidi = $1.32m
2653 / 2921 Cheikh Tidiane Sabaly = NaN
2654 / 2921 Youri Tielemans = $60.50m
2655 / 2921 Kieran Tierney = $35.20m
2656 / 2921 Steffen Tigges = $1.65m
2657 / 2921 Malik Tillman = $1.65m
2658 / 2921 Timothy Tillman = $1.98m
2659 / 2921 David Timor = NaN
2660 / 2921 Jean-Clair Todibo = $22.00m
2661 / 2921 Umut Tohumcu = -
2662 / 2921 Karl Toko Ekambi = $16.50m
2663 / 2921 Corentin Tolisso = $16.50m
2664 / 2921 Jeremy Toljan = $5.50m
2665 / 2921 Rafael Tolói = $8.80m
2666 / 2921 Raúl de Tomás = $27.50m
2667 / 2921 Tiago Tomás = $7.70m
2668 / 2921 Takehiro Tomiyasu = $27.50m
2669 / 2921 Takehiro Tomiyasu = NaN
2670 / 2921 James Tomkins = $1.65m
2671 / 2921 Fikayo Tomori = $55.00m
2672 / 2921 Sandro Tonali = $55.00m
2673 / 2921 Lorenzo Tonelli = $1.10m
2674 / 2921 Ivan Toney = $38.50m
2675 / 2921 Ernesto Torregrossa = NaN
2676 / 2921 Lucas Torreira = $22.00m
2677 / 2921 Raúl Torrente = $3.30m
2678 / 2921 Ferrán Torres = NaN
2679 / 2921 Ferrán Torres = $49.50m
2680 / 2921 Óliver Torres = $8.80m
2681 / 2921 Pau Torres = $55.00m
2682 / 2921 Roberto Torres = $3.30m
2683 / 2921 Lucas Torró = $5.50m
2684 / 2921 Jordan Torunarigha = NaN
2685 / 2921 Cenk Tosun = $2.20m
2686 / 2921 Abdoulaye Touré = NaN
2687 / 2921 Almamy Touré = NaN
2688 / 2921 Lucas Tousart = $9.90m
2689 / 2921 Yoann Touzghar = $660Th.
2690 / 2921 Andros Townsend = $5.50m
2691 / 2921 Adama Traoré = NaN
2692 / 2921 Adama Traoré = $22.00m
2693 / 2921 Amadou Traore = $440Th.
2694 / 2921 Bertrand Traoré = $17.60m
2695 / 2921 Boubacar Traoré = $1.98m
2696 / 2921 Charles Traore = $1.65m
2697 / 2921 Hamari Traoré = $11.00m
2698 / 2921 Ismaël Traoré = $660Th.
2699 / 2921 Kevin Trapp = NaN
2700 / 2921 Miguel Trauco = $1.98m
2701 / 2921 Óscar Trejo = $3.30m
2702 / 2921 Trézéguet = NaN
2703 / 2921 Manu Trigueros = $15.40m
2704 / 2921 Simone Trimboli = $660Th.
2705 / 2921 Christopher Trimmel = $990Th.
2706 / 2921 Francisco Trincão = NaN
2707 / 2921 Kieran Trippier = NaN
2708 / 2921 Kieran Trippier = NaN
2709 / 2921 William Troost-Ekong = $4.40m
2710 / 2921 Leandro Trossard = $22.00m
2711 / 2921 Adrien Truffert = $13.20m
2712 / 2921 Philipp Tschauner = $330Th.
2713 / 2921 Kostas Tsimikas = $16.50m
2714 / 2921 Axel Tuanzebe = NaN
2715 / 2921 Axel Tuanzebe = $7.70m
2716 / 2921 Ozan Tufan = NaN
2717 / 2921 Beñat Turrientes = $3.30m
2718 / 2921 Tuta = NaN
2719 / 2921 Christos Tzolis = $8.80m
2720 / 2921 Iyenoma Udogie = NaN
2721 / 2921 Matthieu Udol = $2.75m
2722 / 2921 Ike Ugbo = $5.50m
2723 / 2921 Chimuanya Ugochukwu = NaN
2724 / 2921 Hwang Ui-jo = NaN
2725 / 2921 Anthony Ujah = $550Th.
2726 / 2921 Maximilian Ullmann = $2.20m
2727 / 2921 Sven Ulreich = $990Th.
2728 / 2921 Samuel Umtiti = $2.20m
2729 / 2921 Enes Ünal = $27.50m
2730 / 2921 Cengiz Ünder = $24.20m
2731 / 2921 Dayot Upamecano = $44.00m
2732 / 2921 Benjamin Uphoff = $550Th.
2733 / 2921 Kacper Urbanski = $220Th.
2734 / 2921 Jere Uronen = $2.20m
2735 / 2921 Mark Uth = $3.30m
2736 / 2921 Myrto Uzuni = $3.30m
2737 / 2921 Antonio Vacca = $770Th.
2738 / 2921 Álvaro Vadillo = NaN
2739 / 2921 Óscar Valentín = $3.30m
2740 / 2921 Germán Valera = $1.65m
2741 / 2921 Yan Valery = $2.75m
2742 / 2921 Martin Valjent = $4.40m
2743 / 2921 Jesús Vallejo = $3.30m
2744 / 2921 Manu Vallejo = $3.30m
2745 / 2921 Manu Vallejo = NaN
2746 / 2921 Federico Valverde = $77.00m
2747 / 2921 Vanderson = NaN
2748 / 2921 Zinho Vanheusden = $8.80m
2749 / 2921 Jonathan Varane = NaN
2750 / 2921 Raphaël Varane = NaN
2751 / 2921 Jamie Vardy = $5.50m
2752 / 2921 Ruben Vargas = $8.80m
2753 / 2921 Sebastian Vasiliadis = $1.98m
2754 / 2921 Johan Vásquez = $4.40m
2755 / 2921 Denis Vavro = NaN
2756 / 2921 José Luis García Vayá = NaN
2757 / 2921 Jesus Vazquez = $5.50m
2758 / 2921 Kevin Vázquez = $2.20m
2759 / 2921 Lucas Vázquez = $13.20m
2760 / 2921 Matías Vecino = $3.85m
2761 / 2921 Gabriel Veiga = $1.65m
2762 / 2921 Miguel Veloso = $990Th.
2763 / 2921 Joël Veltman = $11.00m
2764 / 2921 Mickey van de Ven = $4.40m
2765 / 2921 Unai Vencedor Paris = NaN
2766 / 2921 Lorenzo Venuti = $3.30m
2767 / 2921 Daniele Verde = $5.50m
2768 / 2921 Simone Verdi = NaN
2769 / 2921 Simone Verdi = $5.50m
2770 / 2921 Gonzalo Verdú = $770Th.
2771 / 2921 Jordan Veretout = $18.70m
2772 / 2921 Edoardo Vergani = $660Th.
2773 / 2921 Vincent Vermeij = $440Th.
2774 / 2921 Marco Verratti = NaN
2775 / 2921 Valerio Verre = NaN
2776 / 2921 Valerio Verre = $2.75m
2777 / 2921 Freddie Veseli = $440Th.
2778 / 2921 Mikel Vesga = NaN
2779 / 2921 Jannik Vestergaard = $14.30m
2780 / 2921 Rúben Vezo = $4.40m
2781 / 2921 Guglielmo Vicario = $11.00m
2782 / 2921 Aleix Vidal = $2.20m
2783 / 2921 Arturo Vidal = $2.20m
2784 / 2921 Nacho Vidal = $5.50m
2785 / 2921 Gabriel Vidovic = $2.20m
2786 / 2921 Ronaldo Vieira = $3.85m
2787 / 2921 Nick Viergever = $880Th.
2788 / 2921 Luca Vignali = NaN
2789 / 2921 Emanuel Vignato = $5.50m
2790 / 2921 Dídac Vilà = $660Th.
2791 / 2921 Tonny Vilhena = $5.50m
2792 / 2921 Daniel Villahermosa = $220Th.
2793 / 2921 Asier Villalibre = NaN
2794 / 2921 Gonzalo Villar = $5.50m
2795 / 2921 Matías Viña = $8.25m
2796 / 2921 Nicolas Viola = $660Th.
2797 / 2921 Mattia Viti = $7.70m
2798 / 2921 Vitolo = NaN
2799 / 2921 Daniel Vivian = NaN
2800 / 2921 Dušan Vlahović = NaN
2801 / 2921 Dušan Vlahović = $93.50m
2802 / 2921 Nikola Vlašić = $24.20m
2803 / 2921 Andreas Voglsammer = $1.32m
2804 / 2921 Kevin Vogt = $3.85m
2805 / 2921 Mërgim Vojvoda = $6.05m
2806 / 2921 Kevin Volland = $24.20m
2807 / 2921 Cristian Volpato = -
2808 / 2921 Aster Vranckx = $11.00m
2809 / 2921 Stefan de Vrij = $30.80m
2810 / 2921 Šime Vrsaljko = $5.50m
2811 / 2921 Nikola Vukčević = $1.10m
2812 / 2921 Matěj Vydra = $2.20m
2813 / 2921 Walace = NaN
2814 / 2921 Theo Walcott = $1.98m
2815 / 2921 Luca Waldschmidt = $7.70m
2816 / 2921 Kyle Walker = $19.80m
2817 / 2921 Kyle Walker-Peters = $24.20m
2818 / 2921 Sebastian Walukiewicz = $5.50m
2819 / 2921 Aaron Wan-Bissaka = NaN
2820 / 2921 Paul Wanner = $2.20m
2821 / 2921 Danny Ward = $6.60m
2822 / 2921 Joel Ward = $2.20m
2823 / 2921 James Ward-Prowse = $35.20m
2824 / 2921 Magnus Warming = $990Th.
2825 / 2921 Daniel Wass = $6.60m
2826 / 2921 Daniel Wass = NaN
2827 / 2921 Ollie Watkins = $38.50m
2828 / 2921 Timothy Weah = $13.20m
2829 / 2921 Adam Webster = $18.70m
2830 / 2921 Wout Weghorst = $15.40m
2831 / 2921 Wout Weghorst = NaN
2832 / 2921 Noah Weißhaupt = $1.87m
2833 / 2921 Danny Welbeck = $4.40m
2834 / 2921 Timo Werner = $38.50m
2835 / 2921 Ashley Westwood = $2.20m
2836 / 2921 Ben White = $44.00m
2837 / 2921 Silvan Widmer = $5.50m
2838 / 2921 Georginio Wijnaldum = NaN
2839 / 2921 Jetro Willems = $1.10m
2840 / 2921 Brandon Williams = NaN
2841 / 2921 Iñaki Williams = NaN
2842 / 2921 Neco Williams = $8.80m
2843 / 2921 Nico Williams = NaN
2844 / 2921 Joe Willock = NaN
2845 / 2921 Callum Wilson = NaN
2846 / 2921 Patrick Wimmer = $8.80m
2847 / 2921 Jonas Wind = $15.40m
2848 / 2921 Marten Winkler = $193Th.
2849 / 2921 Harry Winks = $16.50m
2850 / 2921 Frederik Winther = $1.10m
2851 / 2921 Florian Wirtz = $77.00m
2852 / 2921 Yoane Wissa = $13.20m
2853 / 2921 Axel Witsel = $4.40m
2854 / 2921 Hannes Wolf = NaN
2855 / 2921 Marius Wolf = $7.15m
2856 / 2921 Luca Wollschläger = -
2857 / 2921 Jeong Woo-yeong = NaN
2858 / 2921 Chris Wood = NaN
2859 / 2921 Chris Wood = NaN
2860 / 2921 Freddie Woodman = NaN
2861 / 2921 Christopher Wooh = $3.30m
2862 / 2921 Joscha Wosz = NaN
2863 / 2921 Xeka = NaN
2864 / 2921 Granit Xhaka = $22.00m
2865 / 2921 Sekou Yansané = NaN
2866 / 2921 Andriy Yarmolenko = $2.75m
2867 / 2921 Yusuf Yazıcı = NaN
2868 / 2921 Kelvin Yeboah = $5.50m
2869 / 2921 Gerard Yepes = $275Th.
2870 / 2921 Burak Yılmaz = $1.65m
2871 / 2921 Okay Yokuşlu = $3.30m
2872 / 2921 Okay Yokuşlu = NaN
2873 / 2921 Leny Yoro = $165Th.
2874 / 2921 Maya Yoshida = $1.65m
2875 / 2921 Ashley Young = $1.10m
2876 / 2921 Nathan Young-Coombes = -
2877 / 2921 Zaydou Youssouf = $4.40m
2878 / 2921 Mattia Zaccagni = NaN
2879 / 2921 Mattia Zaccagni = $19.80m
2880 / 2921 Dan-Axel Zagadou = $9.90m
2881 / 2921 Wilfried Zaha = $41.80m
2882 / 2921 Denis Zakaria = NaN
2883 / 2921 Denis Zakaria = $29.70m
2884 / 2921 Joseba Zaldúa = $4.40m
2885 / 2921 Nicola Zalewski = $13.20m
2886 / 2921 Andre-Frank Zambo Anguissa = NaN
2887 / 2921 Nicolò Zaniolo = $36.30m
2888 / 2921 Alessandro Zanoli = $4.40m
2889 / 2921 Mattia Zanotti = -
2890 / 2921 Duván Zapata = $30.80m
2891 / 2921 Gabriele Zappa = $4.18m
2892 / 2921 Davide Zappacosta = $9.90m
2893 / 2921 Oier Zarraga = NaN
2894 / 2921 Simone Zaza = $1.10m
2895 / 2921 Akim Zedadka = $4.40m
2896 / 2921 Deyovaisio Zeefuik = NaN
2897 / 2921 Marvin Zeegelaar = $880Th.
2898 / 2921 Arber Zeneli = $4.95m
2899 / 2921 Robin Zentner = $5.50m
2900 / 2921 Andi Zeqiri = $2.53m
2901 / 2921 Mehdi Zerkane = $1.65m
2902 / 2921 Edon Zhegrova = $5.50m
2903 / 2921 Luca Zidane = $2.20m
2904 / 2921 Piotr Zieliński = $44.00m
2905 / 2921 David Zima = $7.70m
2906 / 2921 Christoph Zimmermann = $1.10m
2907 / 2921 Oleksandr Zinchenko = $27.50m
2908 / 2921 Hakim Ziyech = $30.80m
2909 / 2921 Jeroen Zoet = $935Th.
2910 / 2921 Simon Zoller = $1.21m
2911 / 2921 Nadir Zortea = $1.54m
2912 / 2921 Banhie Zoukrou = NaN
2913 / 2921 Kurt Zouma = $30.80m
2914 / 2921 Petar Zovko = $110Th.
2915 / 2921 Igor Zubeldia = $16.50m
2916 / 2921 Martín Zubimendi = $33.00m
2917 / 2921 Szymon Żurkowski = $7.70m
2918 / 2921 Martin Ødegaard = $49.50m
2919 / 2921 Milan Đurić = NaN
2920 / 2921 Filip Đuričić = NaN


# load df_value from csv
df_values = load_csv(r"C:\Python_Projects\CSV\df_values_20_06_2022.csv")


# Test export function
path = "C:\Python_Projects\CSV"
filename = "df_values_20_06_2022"
export_csv(df_values_backup,path,filename)


# Check Number of NaN Value in value column
df_values["value"].isna().sum()


# remove "Unnamed: 0" from df_values
df_values = df_values.drop("Unnamed: 0", axis=1)


#Create backup datafarame
df_values_backup = df_values.copy()
df_stats_backup = df_stats.copy()
df_passing_backup = df_passing.copy()
df_shooting_backup = df_shooting.copy()
df_creation_backup = df_creation.copy()
df_defense_backup = df_defense.copy()
df_miscellaneous_backup = df_miscellaneous.copy()


#df_values = df_values_backup
#df_stats = df_stats_backup
#df_passing = df_passing_backup
#df_shooting = df_shooting_backup
#df_creation = df_creation_backup
#df_defense = df_defense_backup
#df_miscellaneous = df_miscellaneous_backup


# Check if dataframe columns
df_defense.columns

Index(['player_name', 'nationality', 'position', 'team', 'league', 'age',
       'birth_year', 'minutes_played_by_90m', 'total_tackels',
       'total_tackels_won', 'total_defensive_tackels', 'total_mid_tackels',
       'total_attack_tackels', 'total_dribblers_tackled',
       'total_dribbles_contested', 'dribbles_tackeld_by_percentage',
       'total_dribbled_past', 'total_pressures', 'total_successful_pressures',
       'successful_pressure_percentage', 'total_pressure_defense',
       'total_pressure_mid', 'total_pressure_attack', 'total_blocks',
       'total_shots_blocked', 'total_shots_saved', 'total_passes_blocked',
       'total_interceptions', 'total_tackels_plus_interceptions',
       'total_clearances', 'total_mistakes', 'matches'],
      dtype='object')


# create new value column in each statistics dataframe
df_stats = df_stats.assign(value="")
df_passing = df_passing.assign(value="")
df_shooting = df_shooting.assign(value="")
df_creation = df_creation.assign(value="")
df_defense = df_defense.assign(value="")
df_miscellaneous = df_miscellaneous.assign(value="")


# Check if new columnds created
df_defense.columns

Index(['player_name', 'nationality', 'position', 'team', 'league', 'age',
       'birth_year', 'minutes_played_by_90m', 'total_tackels',
       'total_tackels_won', 'total_defensive_tackels', 'total_mid_tackels',
       'total_attack_tackels', 'total_dribblers_tackled',
       'total_dribbles_contested', 'dribbles_tackeld_by_percentage',
       'total_dribbled_past', 'total_pressures', 'total_successful_pressures',
       'successful_pressure_percentage', 'total_pressure_defense',
       'total_pressure_mid', 'total_pressure_attack', 'total_blocks',
       'total_shots_blocked', 'total_shots_saved', 'total_passes_blocked',
       'total_interceptions', 'total_tackels_plus_interceptions',
       'total_clearances', 'total_mistakes', 'matches', 'value'],
      dtype='object')


# assign value from df_value to each dataframe
i = 0
for value in df_stats["value"]:
    df_stats["value"][i] = df_values["value"][i]
    i = i+1
    
i = 0
for value in df_passing["value"]:
    df_passing["value"][i] = df_values["value"][i]
    i = i+1
    
i = 0
for value in df_shooting["value"]:
    df_shooting["value"][i] = df_values["value"][i]
    i = i+1

i = 0
for value in df_creation["value"]:
    df_creation["value"][i] = df_values["value"][i]
    i = i+1

i = 0
for value in df_defense["value"]:
    df_defense["value"][i] = df_values["value"][i]
    i = i+1

i = 0
for value in df_miscellaneous["value"]:
    df_miscellaneous["value"][i] = df_values["value"][i]
    i = i+1


# check value columns
df_stats[['player_name','age','position','team','value']]


# Export to csv update dataframes

#Export df_stats
path = r"C:\Python_Projects\CSV\update"
filename = "df_stats_21_06_2021"
export_csv(df_stats,path,filename)

#Export df_passing
path = r"C:\Python_Projects\CSV\update"
filename = "df_passing_21_06_2021"
export_csv(df_passing,path,filename)

#Export df_shooting
path = r"C:\Python_Projects\CSV\update"
filename = "df_shooting_21_06_2021"
export_csv(df_shooting,path,filename)

#Export df_creation
path = r"C:\Python_Projects\CSV\update"
filename = "df_creation_21_06_2021"
export_csv(df_creation,path,filename)

#Export df_defense
path = r"C:\Python_Projects\CSV\update"
filename = "df_defense_21_06_2021"
export_csv(df_defense,path,filename)

#Export df_miscellaneous
path = r"C:\Python_Projects\CSV\update"
filename = "df_miscellaneous_21_06_2021"
export_csv(df_miscellaneous,path,filename)

#Export df_values
path = r"C:\Python_Projects\CSV\update"
filename = "df_values_21_06_2021"
export_csv(df_values,path,filename)


len(df_values)

2921


# Drop rows with NaN values for each dataframe

df_values_na = df_values.dropna(axis=0)
df_stats_na = df_stats.dropna(axis=0)
df_passing_na = df_passing.dropna(axis=0)
df_shooting_na = df_shooting.dropna(axis=0)
df_creation_na = df_creation.dropna(axis=0)
df_defense_na = df_defense.dropna(axis=0)
df_miscellaneous_na = df_miscellaneous.dropna(axis=0)


# Export to csv dataframes without NaN values

#Export df_stats_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_stats_na_21_06_2021"
export_csv(df_stats_na,path,filename)

#Export df_passing_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_passing_na_21_06_2021"
export_csv(df_passing_na,path,filename)

#Export df_shooting_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_shooting_na_21_06_2021"
export_csv(df_shooting_na,path,filename)

#Export df_creation_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_creation_na_21_06_2021"
export_csv(df_creation_na,path,filename)

#Export df_defense_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_defense_na_21_06_2021"
export_csv(df_defense_na,path,filename)

#Export df_miscellaneous_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_miscellaneous_na_21_06_2021"
export_csv(df_miscellaneous_na,path,filename)

#Export df_values_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_values_na_21_06_2021"
export_csv(df_values_na,path,filename)


# Show Specific Player Stats
#df_stats.loc[2385]


# Rename Stats DataFrame Columns
# Columns Description:
'''
mp = matches played
mp_start = matches start in squad
minutes = minutes played
mp_90 = matches played 90 minutes
gls = goals
ast = assists
gls_no_pen = goals no penalty
gls_pen = penalty goals
pen_attempted = penalty attempted
y_cards = yellow cards
r_cards = red cards
gls_90 = goals per 90 minutes
ast_90 = assists per 90 minutes
gls_ast_90 = goals and assists per 90 minutes
gls_no_pen_90 = goals without penalty per 90 minutes
gls_ast_no_pen_90 = goals without penalty and assists per 90 minutes
ex_gls = expected goals
ex_gls_no_pen = expected goals without penalty
ex_ast = expected assists
ex_gls_ast_no_pen = expected goals and assists without penalty
ex_ast_90 = expected assists per 90 minutes
ex_gls_ast_90 = expected goals and assists per 90 minutes
ex_gls_no_pen_90 = expected goals without penalty per 90 minutes
ex_gls_ast_no_pen_90 = expected goals and assists without penalty per 90 minutes
'''

df_stats_na.rename(columns = {'player_name':'name', 'nationality':'nation', 'position':'pos', 'birth_year':'born', 'match_played':'mp',
'match_starts':'mp_start', 'minutes_played':'minutes', 'minutes_played_by_90m':'mp_90', 'goals':'gls', 'assists':'ast',
'goals_no_pen':'gls_no_pen', 'goals_pen':'gls_pen', 'pens_att':'pen_attempted', 'yellow_card':'y_cards', 'red_cards':'r_cards',
'goals_per_90':'gls_90', 'assists_per_90':'ast_90', 'goals_and_assists_per_90':'gls_ast_90',
'goals_no_pen_per_90':'gls_no_pen_90', 'goals_no_pen_and_assists_per_90':'gls_ast_no_pen_90', 'expected_goals':'ex_gls',
'expected_goals_no_pen':'ex_gls_no_pen', 'expected_assists':'ex_ast', 'expected_goals_and_assists_no_pen':'ex_gls_ast_no_pen',
'expected_goals_per_90':'ex_gls_90', 'expected_assists_per_90':'ex_ast_90', 'expected_goals_and_assists_per_90':'ex_gls_ast_90',
'expected_goals_no_pen_per_90':'ex_gls_no_pen_90', 'expected_goals_and_assists_no_pen_per_90':'ex_gls_ast_no_pen_90'}, inplace = True)


df_stats_na.columns

Index(['name', 'nation', 'pos', 'team', 'league', 'age', 'born', 'mp',
       'mp_start', 'minutes', 'minutes_90', 'gls', 'ast', 'gls_no_pen',
       'gls_pen', 'pen_attempted', 'y_cards', 'r_cards', 'gls_90', 'ast_90',
       'gls_ast_90', 'gls_no_pen_90', 'gls_ast_no_pen_90', 'ex_gls',
       'ex_gls_no_pen', 'ex_ast', 'ex_gls_ast_no_pen', 'ex_gls_90',
       'ex_ast_90', 'ex_gls_ast_90', 'ex_gls_no_pen_90',
       'ex_gls_ast_no_pen_90', 'matches', 'value'],
      dtype='object')


# Rename Shooting DataFrame Columns
# Columns Description:
'''
shots = total shots
shots_target = shots on target
shots_target_% = shots on target percent
shots_90 = total shots per 90 minutes
shots_target_90 = shots on target per 90 minutes
gls_per_shots = goals per shots
gls_per_shots_target = goals per shots on target
avg_shot_dist = average shot distance
fk_shots = free kick shots
pen_made = penalty made
pen_attempted = penalty attempted
ex_gls = expected goals
ex_gls_no_pen = expected goals without penalty
ex_gls_shots_no_pen = expected goals per shots without penalty
gls_minus_ex_gls = goals minus expected goals
gls_minus_ex_gls_no_pen = goals minus expected goals without penalty
'''

df_shooting_na.rename(columns = {'player_name':'name', 'nationality':'nation', 'position':'pos', 'birth_year':'born',
'match_played':'mp', 'match_by_90m':'mp_90', 'goals':'gls', 'total_shots':'shots', 'shots_on_target':'shots_target',
'shots_on_target_percent':'shots_target_%', 'total_shots_per_90m':'shots_90', 'shots_on_target_per_90m':'shots_target_90',
'goals_per_shots':'gls_shots', 'goals_per_shot_on_target':'gls_shots_target', 'goals_per_shots':'gls_shots',
'goals_per_shot_on_target':'gls_shots_target', 'average_shot_distance_meter':'avg_shot_dist', 'free_kick_shots':'fk_shots',
'pens_made':'pen_made', 'pens_attempted':'pen_attempted', 'expected_goals':'ex_gls', 'expected_goals_no_pens':'ex_gls_no_pen',
'expected_goal_per_shot_no_pens':'ex_gls_shots_no_pen', 'goals_minus_expected_goals':'gls_minus_ex_gls',
'non_pens_goals_minus_expected_pens_goals':'gls_minus_ex_gls_no_pen'}, inplace = True)


df_shooting_na.columns

Index(['name', 'nation', 'pos', 'team', 'league', 'age', 'born', 'mp_90',
       'gls', 'shots', 'shots_target', 'shots_target_%', 'shots_90',
       'shots_target_90', 'gls_shots', 'gls_shots_target', 'avg_shot_dist',
       'fk_shots', 'pen_made', 'pen_attempted', 'ex_gls', 'ex_gls_no_pen',
       'ex_gls_shots_no_pen', 'gls_minus_ex_gls', 'gls_minus_ex_gls_no_pen',
       'matches', 'value'],
      dtype='object')


# Rename Passing DataFrame Columns
# Columns Description:
'''
pass_completed = total pass completed
pass_attempted = total pass attempted
pass_completed_% = pass completed percentage
all_pass_dist = total pass distance
all_pass_dist_forward = total distance forward pass
short_pass_% = shots pass percentage
mid_pass_% = mid pass percentage
long_pass_% = long pass percentage
ex_ast = expected assists
ast_minus_ex_ast = expected assists minus assists
pass_to_shot = pass lead to shot
progressive_pass = forward pass
'''

df_passing_na.rename(columns = {'player_name':'name', 'nationality':'nation', 'position':'pos', 'birth_year':'born',
'match_played':'mp', 'match_by_90m':'mp_90', 'total_pass_completed':'pass_completed', 'total_pass_attempted':'pass_attempted',
'pass_completed_percentage':'pass_completed_%', 'total_pass_dist':'all_pass_dist', 'total_pass_dist_forward':'all_pass_dist_forward',
'short_pass_percentage':'short_pass_%', 'mid_pass_percentage':'mid_pass_%', 'long_pass_percentage':'long_pass_%',
'long_pass_percentage':'long_pass_%', 'assists':'ast', 'expected_assists':'ex_ast', 
'assists_minus_expected_assists':'ast_minus_ex_ast', 'pass_lead_to_shot':'pass_to_shot',
'pass_30_court(na)':'pass_30', 'pass_18_court(na)':'pass_18', 'crosses_18_court(na)':'cross_18',
'progressive_pass(na)':'progressive_pass'}, inplace = True)


df_passing_na.columns

Index(['name', 'nation', 'pos', 'team', 'league', 'age', 'born',
       'minutes_played_by_90m', 'pass_completed', 'pass_attempted',
       'pass_completed_%', 'all_pass_dist', 'all_pass_dist_forward',
       'short_pass_completed', 'short_pass_attempted', 'short_pass_%',
       'mid_pass_completed', 'mid_pass_attempted', 'mid_pass_%',
       'long_pass_completed', 'long_pass_attempted', 'long_pass_%', 'ast',
       'ex_ast', 'ast_minus_ex_ast', 'pass_to_shot', 'pass_30', 'pass_18',
       'cross_18', 'progressive_pass', 'matches', 'value'],
      dtype='object')


# Rename Creation DataFrame Columns
# Columns Description:
'''
shot_created_90 = shots created per 90 minutes
shot_to_attempt = shots lead to attempt
defense_to_attempt = defense action lead to attempt
goal_creation_90 = goal creation per 90 minuts
pass_to_goal = pass lead to goal
miss_pass_to_goal = miss pass lead to goal
dribble_to_goal = dribble action lead to goal
shots_to_goal = shots lead to goal
fouls_to_goal = fouls earned lead to goal
defense_to_goal = defense action lead to goal
'''

df_creation_na.rename(columns = {'player_name':'name', 'nationality':'nation', 'position':'pos', 'birth_year':'born',
'match_played':'mp', 'match_by_90m':'mp_90', 'total_shots_creation':'shot_created', 
'shot_creation_by_90m':'shot_created_90', 'total_success_pass':'pass_completed', 'total_miss_pass':'pass_missed',
'total_success_dribble':'dribble_completed', 'total_shots_lead_to_attempt':'shot_to_attempt',
'total_earned_fouls':'earned_fouls', 'defense_action_lead_to_attempt':'defense_to_attempt', 'total_goal_creation':'goal_creation',
'goal_creation_by_90m':'goal_creation_90', 'total_pass_lead_to_goal':'pass_to_goal', 'total_miss_pass_lead_to_goal':'miss_pass_to_goal',
'total_dribble_lead_to_goal':'dribble_to_goal', 'total_shots_lead_to_goal':'shots_to_goal', 'total_earned_fould_lead_to_goal':'fouls_to_goal',
'defense_action_lead_to_goal':'defense_to_goal'}, inplace = True)


df_creation_na.columns

Index(['name', 'nation', 'pos', 'team', 'league', 'age', 'born',
       'minutes_played_by_90m', 'shot_created', 'shot_created_90',
       'pass_completed', 'pass_missed', 'dribble_completed', 'shot_to_attempt',
       'earned_fouls', 'defense_to_attempt', 'goal_creation',
       'goal_creation_90', 'pass_to_goal', 'miss_pass_to_goal',
       'dribble_to_goal', 'shots_to_goal', 'fouls_to_goal', 'defense_to_goal',
       'matches', 'value'],
      dtype='object')


# Rename miscellaneous DataFrame Columns
# Columns Description:
'''
2y_cards = second yellow card
w_tackels = won tackels
'''

df_miscellaneous_na.rename(columns = {'player_name':'name', 'nationality':'nation', 'position':'pos', 'birth_year':'born',
'match_played':'mp', 'match_by_90m':'mp_90', 'yellow_cards':'y_cards', 'red_cards':'r_cards', 
'second_yellow_card':'2y_cards', 'fouls_drawn':'earned_fouls', 'crosses':'dribble', 'tackels_won':'w_tackels',
'pens_won':'pen_earned', 'pens_conceded':'pen_made', 'own_goals':'own_gls', 'ball_recoveries':'recoveries',
'aerials_won':'air_tackels_won', 'aerials_lost':'air_tackels_loss', 'aerials_won_percent':'air_tackels_won_%'}, inplace = True)


df_miscellaneous_na.columns

Index(['name', 'nation', 'pos', 'team', 'league', 'age', 'born',
       'minutes_by_90', 'y_cards', 'r_cards', '2y_cards', 'fouls_commited',
       'earned_fouls', 'offsides', 'dribble', 'interceptions', 'w_tackels',
       'pen_earned', 'pen_made', 'own_gls', 'recoveries', 'air_tackels_won',
       'air_tackels_loss', 'air_tackels_won_%', 'matches', 'value'],
      dtype='object')


# Rename defense DataFrame Columns
# Columns Description:

'''
w_tackels = won tackels
def_tackels = deffensive tackels
att_tackels = attack tackels
dribble_faced = dribble faced againt
def_pressure = defensive pressure
att_pressure = attack pressure
blk = total blocks
shot_blk = total shot blocked
pass_blk = total pass blocked
'''


df_defense_na.rename(columns = {'player_name':'name', 'nationality':'nation', 'position':'pos', 'birth_year':'born',
'match_played':'mp', 'match_by_90m':'mp_90', 'total_tackels':'tackels', 'total_tackels_won':'w_tackels',
'total_defensive_tackels':'def_tackels', 'total_mid_tackels':'mid_tackels', 'total_attack_tackels':'att_tackels',
'total_dribblers_tackled':'dribble_tackeld', 'total_dribbles_contested':'dribble_faced',
'dribbles_tackeld_by_percentage':'dribble_tackeld_%', 'total_dribbled_past':'dribble_miss_tackels',
'total_pressures':'pressures', 'total_successful_pressures':'good_pressures', 'successful_pressure_percentage':'good_pressures_%',
'total_pressure_defense':'def_pressure', 'total_pressure_mid':'mid_pressure', 'total_pressure_attack':'att_pressure',
'total_blocks':'blk', 'total_shots_blocked':'shot_blk', 'total_shots_saved':'shot_save', 'total_passes_blocked':'pass_blk',
'total_interceptions':'interceptions', 'total_tackels_plus_interceptions':'tackels_and_interceptions',
'total_clearances':'clearances'}, inplace = True)


df_defense_na.columns

Index(['name', 'nation', 'pos', 'team', 'league', 'age', 'born',
       'minutes_played_by_90m', 'tackels', 'w_tackels', 'def_tackels',
       'mid_tackels', 'att_tackels', 'dribble_tackeld', 'dribble_faced',
       'dribble_tackeld_%', 'dribble_miss_tackels', 'pressures',
       'good_pressures', 'good_pressures_%', 'def_pressure', 'mid_pressure',
       'att_pressure', 'blk', 'shot_blk', 'shot_save', 'pass_blk',
       'interceptions', 'tackels_and_interceptions', 'clearances',
       'total_mistakes', 'matches', 'value'],
      dtype='object')


#number of rows before
df_stats_na.shape[0]

2210


'''
check rows with column value = "-"
df_stats_na.loc[df_stats_na['value'] == '-']
'''

df_stats_na = df_stats_na[df_stats_na.value != '-']
df_passing_na = df_passing_na[df_passing_na.value != '-']
df_shooting_na = df_shooting_na[df_shooting_na.value != '-']
df_creation_na = df_creation_na[df_creation_na.value != '-']
df_defense_na = df_defense_na[df_defense_na.value != '-']
df_miscellaneous_na = df_miscellaneous_na[df_miscellaneous_na.value != '-']


#number of rows after
df_stats_na.shape[0]

2173


df_stats_na[df_stats_na.name.duplicated()]


df_stats_na[df_stats_na.name == "João Pedro"]


# Reset Dataframes Index fter dropna

df_stats_na = df_stats_na.reset_index(drop=True)
df_passing_na = df_passing_na.reset_index(drop=True)
df_shooting_na = df_shooting_na.reset_index(drop=True)
df_creation_na = df_creation_na.reset_index(drop=True)
df_defense_na = df_defense_na.reset_index(drop=True)
df_miscellaneous_na = df_miscellaneous_na.reset_index(drop=True)


# Replace player value .Th with K (for each dataframe)
i=0
for row in df_stats_na["value"]:
    df_stats_na["value"][i] = df_stats_na["value"][i].replace("Th.","K")
    i=i+1

i=0
for row in df_passing_na["value"]:
    df_passing_na["value"][i] = df_passing_na["value"][i].replace("Th.","K")
    i=i+1
    
i=0
for row in df_shooting_na["value"]:
    df_shooting_na["value"][i] = df_shooting_na["value"][i].replace("Th.","K")
    i=i+1

i=0
for row in df_creation_na["value"]:
    df_creation_na["value"][i] = df_creation_na["value"][i].replace("Th.","K")
    i=i+1

i=0
for row in df_defense_na["value"]:
    df_defense_na["value"][i] = df_defense_na["value"][i].replace("Th.","K")
    i=i+1

i=0
for row in df_miscellaneous_na["value"]:
    df_miscellaneous_na["value"][i] = df_miscellaneous_na["value"][i].replace("Th.","K")
    i=i+1


# Remove matches column from each dataframe
# Run only once
df_stats_na = df_stats_na.drop(columns = "matches")
df_passing_na = df_passing_na.drop(columns = "matches")
df_shooting_na = df_shooting_na.drop(columns = "matches")
df_creation_na = df_creation_na.drop(columns = "matches")
df_defense_na = df_defense_na.drop(columns = "matches")
df_miscellaneous_na = df_miscellaneous_na.drop(columns = "matches")


# All players position type:
df_stats_na["pos"].unique()

array(['DF', 'MF', 'FW', 'MF,FW', 'FW,MF', 'FW,DF', 'GK', 'DF,MF',
       'MF,DF', 'DF,FW', 'GK,MF'], dtype=object)


# Functio to define player position
'''
Goalkeeper
Defender (Centre-back, Sweeper, Full-back, Wing-back)
Midfielder (Centre midfield, Defensive midfield, Attacking midfield, Wide midfield)
Forward (Centre forward, Second striker, Winger)
'''

def define_position(pos):
    if(pos == "DF"):
        pod = "DEF"
        return pos
    elif(pos == "MF"):
        pos = "MID"
        return pos
    elif(pos == "FW"):
        pos = "ATT"
        return pos
    elif(pos == "MF,FW"):
        pos = "ATT"
        return pos
    elif(pos == "FW,MF"):
        pos = "ATT"
        return pos
    elif(pos == "FW,DF"):
        pos = "DEF"
        return pos
    elif(pos == "GK"):
        return pos
    elif(pos == "DF,MF"):
        pos = "MID"
        return pos
    elif(pos == "MF,DF"):
        pos = "MID"
        return pos
    elif(pos == "DF,FW"):
        pos = "DEF"
        return pos
    elif(pos == "GK,MF"):
        pos = "GK"
        return pos


# Change Player Position by define_position function

i=0
for row in df_stats_na["pos"]:
    position = define_position(df_stats_na["pos"][i])
    df_stats_na["pos"][i] = position
    i=i+1

i=0
for row in df_passing_na["pos"]:
    position = define_position(df_passing_na["pos"][i])
    df_passing_na["pos"][i] = position
    i=i+1

i=0
for row in df_shooting_na["pos"]:
    position = define_position(df_shooting_na["pos"][i])
    df_shooting_na["pos"][i] = position
    i=i+1

i=0
for row in df_creation_na["pos"]:
    position = define_position(df_creation_na["pos"][i])
    df_creation_na["pos"][i] = position
    i=i+1

i=0
for row in df_defense_na["pos"]:
    position = define_position(df_defense_na["pos"][i])
    df_defense_na["pos"][i] = position
    i=i+1

i=0
for row in df_miscellaneous_na["pos"]:
    position = define_position(df_miscellaneous_na["pos"][i])
    df_miscellaneous_na["pos"][i] = position
    i=i+1


# Export Dataframes with Updates Columns

#Export df_stats_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_stats_na_col_23_06_2021"
export_csv(df_stats_na,path,filename)

#Export df_passing_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_passing_na_col_23_06_2021"
export_csv(df_passing_na,path,filename)

#Export df_shooting_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_shooting_na_col_23_06_2021"
export_csv(df_shooting_na,path,filename)

#Export df_creation_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_creation_na_col_23_06_2021"
export_csv(df_creation_na,path,filename)

#Export df_defense_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_defense_na_col_23_06_2021"
export_csv(df_defense_na,path,filename)

#Export df_miscellaneous_na
path = r"C:\Python_Projects\CSV\update"
filename = "df_miscellaneous_na_col_23_06_2021"
export_csv(df_miscellaneous_na,path,filename)


# Function to merge dataframes without duplicate columns and without indexing

def merge_dataframe(df_1,df_2):
    df_merge = pd.DataFrame()
    # difference columns between df_2 to df_1
    diff_cols = df_2.columns.difference(df_1)
    # merge dataframe without duplicate columns
    df_merge = pd.merge(df_1, df_2[diff_cols],left_index=False, right_index=False, how='outer')
    df_merge = df_merge.reset_index(drop=True)
    return df_merge


# create temp copies of dataframes for merge

df_merge_stats_1 = df_stats_na
df_merge_passing_2 = df_passing_na
df_merge_shooting_3 = df_shooting_na
df_merge_defense_4 = df_defense_na

df_merge_creation_5 = df_creation_na
df_merge_miscellaneous_6 = df_miscellaneous_na


# merge 1 and 2
df_merge_12 = merge_dataframe(df_merge_stats_1,df_merge_passing_2)


# merge 12 and 3
df_merge_123 = merge_dataframe(df_merge_12,df_merge_shooting_3)


# merge 123 and 4
df_merge_1234 = merge_dataframe(df_merge_123,df_merge_defense_4)


df_merge_1234.shape

(2173, 91)


# merge 5 and 6 - not work
#df_merge_56 = merge_dataframe(df_merge_creation_5,df_merge_miscellaneous_6)


df_merge_56 = pd.DataFrame()


diff_cols = df_merge_miscellaneous_6.columns.difference(df_merge_creation_5)


df_merge_56 = pd.merge(df_merge_creation_5, df_merge_miscellaneous_6[diff_cols],left_index=True, right_index=True, how='outer')


df_merge_56.shape

(2173, 50)


df_merge_56


#Export merge dataframes
path = r"C:\Python_Projects\CSV\update"
filename = "df_stats_passing_shooting_defense_23_06_2021"
export_csv(df_merge_1234,path,filename)

path = r"C:\Python_Projects\CSV\update"
filename = "df_creation_miscellaneous_23_06_2021"
export_csv(df_merge_56,path,filename)


# Colums to delete from df_merge_1234
'''
born, minutes, minutes_90, pen_attempted, gls_ast_90, gls_no_pen_90, gls_ast_no_pen_90, ex_gls_no_pen, ex_gls_ast_no_pen,
ex_gls_ast_90, ex_gls_no_pen_90, ex_gls_ast_no_pen_90, all_pass_dist, cross_18, long_pass_attempted, long_pass_completed,
mid_pass_attempted, mid_pass_completed, minutes_played_by_90m, pass_18, pass_30, pass_attempted, pass_completed,
pass_to_shot, short_pass_attempted, short_pass_completed, avg_shot_dist, fk_shots, gls_minus_ex_gls_no_pen, gls_shots_target,
shots, shots_target, att_tackels, def_pressure, good_pressures, good_pressures_%, mid_pressure, pressures,
tackels_and_interceptions
'''
df_merge_1234_backup = df_merge_1234.copy()


#car_df.drop(['safety_rating', 'passenger_capacity'], axis = 1, inplace = True)
df_merge_1234 = df_merge_1234.drop(['born','minutes','minutes_90','pen_attempted','gls_ast_90','gls_no_pen_90',
                                  'gls_ast_no_pen_90','ex_gls_no_pen','ex_gls_ast_no_pen','ex_gls_ast_90','ex_gls_no_pen_90',
                                  'all_pass_dist','cross_18','long_pass_attempted','long_pass_completed','mid_pass_attempted',
                                  'mid_pass_completed','minutes_played_by_90m','pass_18','pass_30','pass_attempted',
                                  'pass_completed','pass_to_shot','short_pass_attempted','short_pass_completed',
                                  'short_pass_completed','avg_shot_dist','fk_shots','gls_minus_ex_gls_no_pen',
                                  'gls_shots_target','shots','shots_target','att_tackels','def_pressure','good_pressures',
                                  'good_pressures_%','mid_pressure','pressures','tackels_and_interceptions'], axis=1)


# After remove the unnecessary columns
# Before : 91 columns
# After 53 columns
df_merge_1234.shape

(2173, 53)


df_merge_56 = load_csv(r"C:\Python_Projects\CSV\tests\df_creation_miscellaneous_24_06_2021.csv")


df_merge_56


#df_all_merge = merge_dataframe(df_merge_1234,df_merge_56)

diff_cols_2 = df_merge_56.columns.difference(df_merge_1234.columns)


df_all_merge = pd.merge(df_merge_1234, df_merge_56[diff_cols_2],left_index=True, right_index=True, how='outer')
#df_all_merge = merge_dataframe(df_merge_1234,df_merge_56)


df_all_merge.head()


# Export all_df_merge
path = "C:\Python_Projects\CSV"
filename = "df_all_merge_24_06_2022"
export_csv(df_all_merge,path,filename)


df_all_merge.shape

(2173, 66)


df_all_merge["value"][4]

'$880K'


def convert_value_k_to_m(value):
    value = value.replace('K','')
    value = value.replace('$','')
    value = int(value)
    value = (value/1000)
    value = "$"+str(value)+"m"
    return value


df_all_merge_backup = df_all_merge.copy()


# Conver all K value to M
i=0
for row in df_all_merge["value"]:
    value = df_all_merge["value"][i]
    if "K" in value:
        new_value = convert_value_k_to_m(value)
        df_all_merge["value"][i] = new_value
    i=i+1


df_all_merge["value"].head()

0    $24.20m
1     $1.32m
2     $3.30m
3     $3.30m
4     $0.88m
Name: value, dtype: object


# Export all_df_merge
path = "C:\Python_Projects\CSV"
filename = "df_all_merge_24_06_2022"
export_csv(df_all_merge,path,filename)


# Player Distribution by league
'''
eng Premier League
fr Ligue 1
it Serie A
es La Liga
de Bundesliga
'''
players_dist = df_all_merge["league"].value_counts()


data = players_dist
data.plot(kind="bar")
plt.title("Players league distribution")
plt.xlabel("League")
plt.ylabel("Players")
plt.show()


df_players = df_all_merge.copy()


#'DF', 'MID', 'ATT', 'DEF', 'GK'
def define_position_2(pos):
    if (pos == "DF"):
        pos = "DEF"
        return pos
    elif (pos == "MID"):
        return pos
    elif (pos == "ATT"):
        return pos
    elif (pos == "DEF"):
        return pos
    elif (pos == "GK"):
        return pos


df_all_merge["pos"].unique()

array(['DEF', 'MID', 'ATT', 'GK'], dtype=object)


i=0
for row in df_all_merge["pos"]:
    position = define_position_2(df_all_merge["pos"][i])
    df_all_merge["pos"][i] = position
    i=i+1

C:\Users\PELEGL~1\AppData\Local\Temp/ipykernel_13576/2878682629.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_all_merge["pos"][i] = position


df_all_merge["pos"].unique()

array(['DEF', 'MID', 'ATT', 'GK'], dtype=object)


# Function to substring price
def priceSubstring(value):
    value = value.replace('m','')
    value = value.replace('$','')
    return value


# Change players value by substring
i=0
for row in df_players["value"]:
    value = df_players["value"][i]
    new_value = priceSubstring(value)
    df_players["value"][i] = new_value
    i=i+1

C:\Users\PELEGL~1\AppData\Local\Temp/ipykernel_13576/2656539669.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_players["value"][i] = new_value


# list columns to change to float
cols_to_convert=[
'age',
'mp',
'mp_start',
'gls',
'ast',
'gls_no_pen',
'gls_pen',
'y_cards',
'r_cards',
'gls_90',
'ast_90',
'ex_gls',
'ex_ast',
'ex_gls_90',
'ex_ast_90',
'ex_gls_ast_no_pen_90',
'all_pass_dist_forward',
'ast_minus_ex_ast',
'long_pass_%',
'mid_pass_%',
'pass_completed_%',
'progressive_pass',
'short_pass_%',
'ex_gls_shots_no_pen',
'gls_minus_ex_gls',
'gls_shots',
'mp_90',
'pen_made',
'shots_90',
'shots_target_%',
'shots_target_90',
'att_pressure',
'blk',
'clearances',
'def_tackels',
'dribble_faced',
'dribble_miss_tackels',
'dribble_tackeld',
'dribble_tackeld_%',
'interceptions',
'mid_tackels',
'pass_blk',
'shot_blk',
'shot_save',
'tackels',
'total_mistakes',
'w_tackels',
'2y_cards',
'air_tackels_loss',
'air_tackels_won',
'air_tackels_won_%',
'born',
'dribble',
'dribble_completed',
'dribble_to_goal',
'earned_fouls',
'fouls_commited',
'offsides',
'own_gls',
'shot_created_90'
]


for col in cols_to_convert:
    df_players[col] = pd.to_numeric(df_players[col],errors='coerce').astype('float')


df_players.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2173 entries, 0 to 2172
Data columns (total 66 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   name                   2173 non-null   object 
 1   nation                 2173 non-null   object 
 2   pos                    2173 non-null   object 
 3   team                   2173 non-null   object 
 4   league                 2173 non-null   object 
 5   age                    2173 non-null   float64
 6   mp                     2173 non-null   float64
 7   mp_start               2173 non-null   float64
 8   gls                    2173 non-null   float64
 9   ast                    2173 non-null   float64
 10  gls_no_pen             2173 non-null   float64
 11  gls_pen                2173 non-null   float64
 12  y_cards                2173 non-null   float64
 13  r_cards                2173 non-null   float64
 14  gls_90                 2173 non-null   float64
 15  ast_90                 2173 non-null   float64
 16  ex_gls                 2173 non-null   float64
 17  ex_ast                 2173 non-null   float64
 18  ex_gls_90              2173 non-null   float64
 19  ex_ast_90              2173 non-null   float64
 20  ex_gls_ast_no_pen_90   2173 non-null   float64
 21  value                  2173 non-null   float64
 22  all_pass_dist_forward  2173 non-null   float64
 23  ast_minus_ex_ast       2173 non-null   float64
 24  long_pass_%            2107 non-null   float64
 25  mid_pass_%             2138 non-null   float64
 26  pass_completed_%       2167 non-null   float64
 27  progressive_pass       2173 non-null   float64
 28  short_pass_%           2149 non-null   float64
 29  ex_gls_shots_no_pen    1812 non-null   float64
 30  gls_minus_ex_gls       2173 non-null   float64
 31  gls_shots              1812 non-null   float64
 32  mp_90                  2173 non-null   float64
 33  pen_made               2173 non-null   float64
 34  shots_90               2173 non-null   float64
 35  shots_target_%         1812 non-null   float64
 36  shots_target_90        2173 non-null   float64
 37  att_pressure           2173 non-null   float64
 38  blk                    2173 non-null   float64
 39  clearances             2173 non-null   float64
 40  def_tackels            2173 non-null   float64
 41  dribble_faced          2173 non-null   float64
 42  dribble_miss_tackels   2173 non-null   float64
 43  dribble_tackeld        2173 non-null   float64
 44  dribble_tackeld_%      1932 non-null   float64
 45  interceptions          2173 non-null   float64
 46  mid_tackels            2173 non-null   float64
 47  pass_blk               2173 non-null   float64
 48  shot_blk               2173 non-null   float64
 49  shot_save              2173 non-null   float64
 50  tackels                2173 non-null   float64
 51  total_mistakes         2173 non-null   float64
 52  w_tackels              2173 non-null   float64
 53  2y_cards               2173 non-null   float64
 54  air_tackels_loss       2173 non-null   float64
 55  air_tackels_won        2173 non-null   float64
 56  air_tackels_won_%      1931 non-null   float64
 57  born                   2173 non-null   float64
 58  dribble                2173 non-null   float64
 59  dribble_completed      2173 non-null   float64
 60  dribble_to_goal        2173 non-null   float64
 61  earned_fouls           2173 non-null   float64
 62  fouls_commited         2173 non-null   float64
 63  offsides               2173 non-null   float64
 64  own_gls                2173 non-null   float64
 65  shot_created_90        2173 non-null   float64
dtypes: float64(61), object(5)
memory usage: 1.1+ MB


df_players = df_players.sort_values(by=["value"],ascending=False)
df_players = df_players.reset_index(drop=True)


df_players[["name","mp","league","gls","ast","value"]]


df_players_bck = df_players.copy()


df_players


# Create 4 Dataframe by player position (GK,DEF,MID,ATT)
df_GK = df_players.copy()
df_DEF = df_players.copy()
df_MID = df_players.copy()
df_ATT = df_players.copy()

df_GK = df_GK[df_GK.pos == "GK"]
df_DEF = df_DEF[df_DEF.pos == "DEF"]
df_MID = df_MID[df_MID.pos == "MID"]
df_ATT = df_ATT[df_ATT.pos == "ATT"]

# Sort all dataframe by value
df_GK = df_GK.sort_values(by=["value"],ascending=False)
df_DEF = df_DEF.sort_values(by=["value"],ascending=False)
df_MID = df_MID.sort_values(by=["value"],ascending=False)
df_ATT = df_ATT.sort_values(by=["value"],ascending=False)

# Reset all dataframe index
df_GK = df_GK.reset_index(drop=True)
df_DEF = df_DEF.reset_index(drop=True)
df_MID = df_MID.reset_index(drop=True)
df_ATT = df_ATT.reset_index(drop=True)


# remove unnecessary from each dataframe
df_ATT = df_ATT.drop(columns=["gls","ast","all_pass_dist_forward","gls_shots","mp","dribble_faced","ex_gls","ex_ast",
                              "dribble_miss_tackels","shot_blk","shot_save","dribble_tackeld_%",
                              "w_tackels","own_gls","fouls_commited","born","mp_start","gls_no_pen","gls_pen",
                              "ex_gls_shots_no_pen","dribble"])


df_ATT.shape

(676, 45)


df_MID = df_MID.drop(columns=["mp","gls","ast","ex_gls","ex_ast","gls_no_pen","ex_gls_shots_no_pen",
                             "gls_shots","dribble_faced","dribble_tackeld","born","dribble"])


df_MID.shape

(546, 54)


df_DEF = df_DEF.drop(columns=["mp","gls","ast","gls_no_pen","ex_gls","ex_ast","ex_gls_shots_no_pen",
                             "gls_shots","born","dribble"])


df_DEF.shape

(761, 56)


df_players[["name","age","pos","team","value"]].head(10)


df_ATT[["name","age","pos","team","value"]].head(5)


df_MID[["name","age","pos","team","value"]].head(5)


df_DEF[["name","age","pos","team","value"]].head(5)


pd.DataFrame(df_players.groupby("pos").value.mean())


pd.DataFrame(df_players.groupby(["team", "league"]).value.mean().sort_values(ascending=False).head(5))


pd.DataFrame(df_players.groupby("team").value.sum().sort_values(ascending=False).head(5))


df_players.hist(column="age")

array([[<AxesSubplot:title={'center':'age'}>]], dtype=object)


df_players.plot(x="value",y="age")
plt.show()


plt.figure(figsize=(8,8))
loc=df_players.pos.value_counts()
loc.plot(kind='pie' ,autopct='%.1f',fontsize=12,subplots=True)
plt.title("Player's Positions Distribution")
plt.ylabel("")
plt.show()


sns.set_theme(color_codes=False)
plt.figure(figsize=(10,6))
sns.regplot(x="age",y="value",data=df_players, x_estimator=np.mean)
plt.show()


df_ATT.describe()


df_MID.describe()


df_DEF.describe()


df_ATT[["name", "team","value"]].head(5), df_MID[["name", "team","value"]].head(5),df_DEF[["name", "team","value"]].head(5)

(              name             team  value
 0   Erling Haaland         Dortmund  165.0
 1  Vinicius Júnior      Real Madrid  110.0
 2       Phil Foden  Manchester City   99.0
 3       Harry Kane        Tottenham   99.0
 4    Mohamed Salah        Liverpool   99.0,
                       name             team  value
 0          Kevin De Bruyne  Manchester City   93.5
 1           Joshua Kimmich    Bayern Munich   88.0
 2              Declan Rice         West Ham   88.0
 3          Jude Bellingham         Dortmund   88.0
 4  Sergej Milinković-Savić            Lazio   77.0,
                      name             team  value
 0  Trent Alexander-Arnold        Liverpool   88.0
 1              Rúben Dias  Manchester City   82.5
 2        Matthijs de Ligt         Juventus   77.0
 3         Alphonso Davies    Bayern Munich   77.0
 4            João Cancelo  Manchester City   71.5)


df_ATT.plot(x="shots_target_%", y="value" ,kind="scatter")

*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.

<AxesSubplot:xlabel='shots_target_%', ylabel='value'>


df_ATT.plot(x="value",y=["gls_90","ex_gls_90","shots_target_90","shots_90"])
plt.show()


# ATT Plot gls_90 & ex_gls_90
df_ATT.plot(x="value",y=["gls_90","ex_gls_90"])
plt.show()


# MID Plot gls_90 & ex_gls_90
df_MID.plot(x="value",y=["gls_90","ex_gls_90","ast_90","ex_ast_90"])
plt.show()


# MID dribble and shot created
df_MID.plot(x="value",y=["dribble_completed","shot_created_90"])
plt.show()


# ATT Plot gls_90 & ex_gls_90
df_MID.plot(x="value",y=["tackels","def_tackels","blk"])
plt.show()


#ATT Heatmap

plt.figure(figsize=(30,18))
heatmap = sns.heatmap(df_ATT.corr(), vmin=-1, vmax=1, annot=True )
heatmap.set_title("ATT Correlation Heatmap", fontdict={"fontsize":18});


#MID Heatmap

plt.figure(figsize=(30,18))
heatmap = sns.heatmap(df_MID.corr(), vmin=-1, vmax=1, annot=True )
heatmap.set_title("ATT Correlation Heatmap", fontdict={"fontsize":18});


# DEF Heatmap

plt.figure(figsize=(30,20))
heatmap = sns.heatmap(df_DEF.corr(), vmin=-1, vmax=1, annot=True )
heatmap.set_title("ATT Correlation Heatmap", fontdict={"fontsize":18});


plt.figure(figsize=(30,20))
sns.heatmap(df_ATT.corr(),vmin=-1,vmax=1,linewidths=.5, annot=True );
#plt.yticks(rotation=360)
plt.show()


plt.figure(figsize=(30,20))
sns.heatmap(df_MID.corr(),vmin=-1,vmax=1,linewidths=.5, annot=True );
#plt.yticks(rotation=360)
plt.show()


plt.figure(figsize=(30,20))
sns.heatmap(df_DEF.corr(),vmin=-1,vmax=1,linewidths=.5, annot=True );
#plt.yticks(rotation=360)
plt.show()


# Export ATT, MID, DEF Dataframes for Backup

# Export correlatio table to csv
path = "C:\Python_Projects\CSV"
filename = "df_ATT"
export_csv(df_ATT,path,filename)

# Export correlatio table to csv
path = "C:\Python_Projects\CSV"
filename = "df_MID"
export_csv(df_MID,path,filename)

# Export correlatio table to csv
path = "C:\Python_Projects\CSV"
filename = "df_DEF"
export_csv(df_DEF,path,filename)


df_corr = df_players.corr()


# Export correlatio table to csv
path = "C:\Python_Projects\CSV"
filename = "df_corr"
export_csv(df_corr,path,filename)


df_ATT_learn = df_ATT.copy()


df_ATT_learn.columns

Index(['name', 'nation', 'pos', 'team', 'league', 'age', 'y_cards', 'r_cards',
       'gls_90', 'ast_90', 'ex_gls_90', 'ex_ast_90', 'ex_gls_ast_no_pen_90',
       'value', 'ast_minus_ex_ast', 'long_pass_%', 'mid_pass_%',
       'pass_completed_%', 'progressive_pass', 'short_pass_%',
       'gls_minus_ex_gls', 'mp_90', 'pen_made', 'shots_90', 'shots_target_%',
       'shots_target_90', 'att_pressure', 'blk', 'clearances', 'def_tackels',
       'dribble_tackeld', 'interceptions', 'mid_tackels', 'pass_blk',
       'tackels', 'total_mistakes', '2y_cards', 'air_tackels_loss',
       'air_tackels_won', 'air_tackels_won_%', 'dribble_completed',
       'dribble_to_goal', 'earned_fouls', 'offsides', 'shot_created_90'],
      dtype='object')


# Drop not numeric columns
df_ATT_learn = df_ATT_learn.drop(columns=["name","nation","pos","team","league"])


# For linear regression the data can't have NaN values !!
df_ATT_learn = df_ATT_learn.dropna()
print(df_ATT_learn.shape)

(616, 40)


TRAINING_FEATURES_ATT = df_ATT_learn.columns[df_ATT_learn.columns != "value"]
TARGET_FEATURE_ATT = "value"

X = df_ATT_learn[TRAINING_FEATURES_ATT]
y = df_ATT_learn[TARGET_FEATURE_ATT]

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.15)

print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)

(523, 39) (523,)
(93, 39) (93,)


# train our model
lin_reg_ATT = linear_model.LinearRegression()
lin_reg_model_ATT = lin_reg_ATT.fit(X_train, y_train)
lin_reg_predict_ATT = lin_reg_ATT.predict(X_test)


lin_reg_predict_ATT

array([  9.16846808,  14.05253798,  13.56061326,  11.89237159,
        45.74983399,  10.14806937,  -5.01500465,  20.4460499 ,
         8.20956592,   1.14493127,  25.44299794,  17.48361889,
        33.7937492 ,  21.01507098,  20.1685458 ,  15.84907536,
        19.26894559,  10.58974906,  13.48942501,  21.95271694,
         5.45334671,   6.20187566,   6.19050485,   6.80658242,
        -2.87244425,   2.48622091,  20.02191575,   9.21999697,
        11.07605871,  -4.0502088 ,  23.60615383,   3.25922493,
        34.43031683,   0.32195009,  25.1025851 ,  30.0225288 ,
        15.8798913 ,  17.45430388,   3.00688135,   2.88995163,
        19.59576619,  21.9163746 ,  14.52399574,  13.20076378,
        -4.24544813,   2.74907739,  30.88727267,  13.32373144,
        15.22295949,  22.28144114,  69.55499689,  13.40107099,
        31.78209479,   5.95702914,  25.42057748,   8.56682115,
        35.55624494,  -6.95955365,  32.99047951,   9.82742681,
       -13.56159017,  22.04072642,  21.20536135,  11.96262728,
        23.99366998,   8.19110901,  18.95633954,   7.55300233,
         3.54656651,  26.18523574,  67.70561222,  14.48041367,
        12.92422703,  21.72033351,  23.3113712 ,   4.24818933,
         5.64437605,   2.76531522,  42.08827826,   1.18138004,
        26.36820533,  42.59215357,  27.9904607 ,   1.40371546,
        10.89239977,   9.65856629,  15.6665039 ,   6.58853523,
        12.27879537,  24.11735914,   6.29960994,  24.41216296,
        14.89127538])


print("Score: ", lin_reg_model_ATT.score(X_test, y_test))

Score:  0.5113984280801751


pd_diff_ATT = pd.DataFrame(X_test)
pd_diff_ATT["value"] = y_test
pd_diff_ATT["predicted_value"] = lin_reg_predict_ATT
pd_diff_ATT = pd_diff_ATT.reset_index()


pd_diff_ATT[["value","predicted_value"]]


df_MID_learn = df_MID.copy()


# Drop not numeric columns
df_MID_learn = df_MID_learn.drop(columns=["name","nation","pos","team","league"])


# For linear regression the data can't have NaN values !!
df_MID_learn = df_MID_learn.dropna()
print(df_MID_learn.shape)

(484, 49)


TRAINING_FEATURES_MID = df_MID_learn.columns[df_MID_learn.columns != "value"]
TARGET_FEATURE_MID = "value"

X = df_MID_learn[TRAINING_FEATURES_MID]
y = df_MID_learn[TARGET_FEATURE_MID]

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3)

print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)

(338, 48) (338,)
(146, 48) (146,)


# train our model
lin_reg_MID = linear_model.LinearRegression()
lin_reg_model_MID = lin_reg_MID.fit(X_train, y_train)
lin_reg_predict_MID = lin_reg_MID.predict(X_test)


lin_reg_predict_MID

array([ 12.64792362,  38.72239958,  28.55077392,  -8.60018675,
         5.79650799,  35.50079393,   5.60894114,   5.73027594,
         4.0290291 ,   8.1084347 ,  10.59838121,  -2.34900993,
        24.06908975,   0.36838757,  -5.49654012,   9.52053339,
         9.82457899,   9.00766857,   9.99327089,  -8.29468775,
        -4.12887496,  18.58161738,  12.66803937,  11.80589887,
        -1.5720129 ,  17.24839419,  -3.56618937,  -5.5588168 ,
        24.1554086 ,   3.54010759,  10.32105858,  22.35600139,
        17.15980189,   4.67496316,   3.28404781,   5.73508996,
        -4.66181742,  10.4982049 ,   2.64792166,  33.29564403,
        18.89175462,  11.90394898,  18.76530439,   4.2656094 ,
         3.9564411 ,  10.05510322,   6.26296805,  -1.25792942,
         4.94760967,   2.93055066,   0.04696671,  42.79612941,
         7.11868539,   4.86245892,  10.25418888,   6.30366078,
        12.40208686,   2.76695612,  26.74983109,  21.00887202,
         3.0190692 ,  -3.22254256,  12.49564003,  22.27307674,
        16.67944016,   0.86495844,   8.53696167, -11.26419029,
        26.2995354 ,  11.90524674,  22.62911816,  15.72768186,
         8.9829426 ,   4.24192093,  10.71268037,  19.31681696,
         5.62128261,   9.39758641,   1.95078847,  15.23123284,
        12.12706106,   8.71889542,  41.63807491,  12.78957547,
        12.48590507,  20.30236588,  45.89369676,  27.42255536,
         3.99927557,  -3.72691512,   5.33484268,   6.96390983,
        14.33955202,   3.39353533,  -7.3591449 ,  11.86641479,
         4.72213546,  29.52962058,  30.8305463 ,  12.02830394,
        18.67765104,  24.65622192,  11.22348532,  -2.21743868,
        17.12968546,  21.98657139,  42.19659915,   3.15483016,
        11.22788284,  12.43914837,   8.23946554,  26.43617556,
        20.72286666,  12.26488248,  10.30406675,  12.18582045,
        25.97441863,  16.18676765,  26.519266  ,   8.92659526,
        36.74942612,  13.62228017,  11.07573544,  15.93703625,
        12.37522211,  10.27791876,  14.98246245,   2.67129943,
         5.53360198,  15.81492595,   5.33420572,  25.89798202,
         6.46093936,   2.31284307,   3.6491    ,  -8.55937146,
        46.16805042,  -6.14849768,   1.50649935,  22.30848205,
         0.74362476,   9.7018146 ,  22.48767017,  27.61015515,
         4.54853059,  15.81267793])


print("Score: ", lin_reg_model_MID.score(X_test, y_test))

Score:  0.5132970122756499


pd_diff_MID = pd.DataFrame(X_test)
pd_diff_MID["value"] = y_test
pd_diff_MID["predicted_value"] = lin_reg_predict_MID
pd_diff_MID = pd_diff_MID.reset_index()


pd_diff_MID[["value","predicted_value"]]


df_DEF_learn = df_DEF.copy()


# Drop not numeric columns
df_DEF_learn = df_DEF_learn.drop(columns=["name","nation","pos","team","league"])


# For linear regression the data can't have NaN values !!
df_DEF_learn = df_DEF_learn.dropna()
print(df_DEF_learn.shape)

(670, 51)


TRAINING_FEATURES_DEF = df_DEF_learn.columns[df_DEF_learn.columns != "value"]
TARGET_FEATURE_DEF = "value"

X = df_DEF_learn[TRAINING_FEATURES_DEF]
y = df_DEF_learn[TARGET_FEATURE_DEF]

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2)

print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)

(536, 50) (536,)
(134, 50) (134,)


# train our model
lin_reg_DEF = linear_model.LinearRegression()
lin_reg_model_DEF = lin_reg_DEF.fit(X_train, y_train)
lin_reg_predict_DEF = lin_reg_DEF.predict(X_test)


lin_reg_predict_DEF

array([  0.36665636,  34.02548617,   8.79139819,   4.01905149,
        46.46851534,  27.46365833,  11.84836649,   8.12662427,
        39.70078443,   5.58111272,   2.82027598,  -7.32680836,
        16.96454058,  18.81666749,  14.41240635,   4.03955094,
        11.50607128,  -7.05087898,   3.39432662,  28.42818476,
         6.27321465,  18.90014731,   6.13639123,  13.63980875,
        16.56090363,   4.66751004,  24.06924204,  26.65767859,
        21.39495449,  27.21486276,  13.53632485,   4.02284347,
         2.03509894,  26.49423288,   2.63511479,   4.72918058,
        24.49232262,   0.23716576,  18.9770049 ,  -0.27790225,
        13.86371186,  22.5261842 ,  16.23830783,   6.62561989,
         6.19420731,  33.48233627,   0.62780044,   2.31318101,
         6.27689307,  11.54855987,  13.2182332 ,  12.79879426,
        -2.99937225,  18.05740177,  21.18299115,  26.74831531,
         4.93045346,   9.67838718,  15.52830239,  27.20168374,
        15.9961214 ,  16.75994492,  21.95895739,   8.48500741,
        12.0334459 ,  13.91088813,  10.88927082,   9.81931651,
        18.64202975,  12.23093594,  31.68417141,  16.69413971,
         3.79326857,  -0.10711082,  26.7694942 ,  13.60458649,
         8.10222217,  -2.90855716,   5.57067931,  14.74106916,
         1.1268844 ,   1.38427021,   6.7531752 ,  15.97197187,
         6.55638607,  19.25068345,   9.18654879,   4.98188499,
        -4.698394  ,  -5.85240556, -15.15384255,  38.95275247,
         4.46438689,   5.50448903,  18.70959732,  22.54573836,
        27.32792389,  10.97388497,  15.29853361,   2.63772229,
         5.31821909,  19.12723056,   9.0757713 ,  21.02891288,
        13.07649851,  17.79710312,  13.88394549,  -1.39717287,
        -4.87660926,  26.6230643 ,   4.21183165,   8.87461738,
        11.49697866,   6.79394142,   9.4438177 ,  19.10746261,
        11.41135615,  -1.05417805,  11.13173411,   7.55911182,
         2.56642855,  11.13777361,   4.88622798,  10.84451109,
         8.5830203 ,  16.51541807,  11.61017559,  -5.58306873,
         5.53914273,  15.9061756 ,  14.41757846,  15.29921129,
        36.14537286,   9.1794584 ])


print("Score: ", lin_reg_model_DEF.score(X_test, y_test))

Score:  0.4781415285530951


pd_diff_DEF = pd.DataFrame(X_test)
pd_diff_DEF["value"] = y_test
pd_diff_DEF["predicted_value"] = lin_reg_predict_DEF
pd_diff_DEF = pd_diff_DEF.reset_index()


pd_diff_DEF[["value","predicted_value"]]

	age	y_cards	r_cards	gls_90	ast_90	ex_gls_90	ex_ast_90	ex_gls_ast_no_pen_90	value	ast_minus_ex_ast	...	total_mistakes	2y_cards	air_tackels_loss	air_tackels_won	air_tackels_won_%	dribble_completed	dribble_to_goal	earned_fouls	offsides	shot_created_90
count	676.000000	676.000000	676.000000	676.000000	676.000000	676.000000	676.000000	676.000000	676.000000	676.000000	...	676.000000	676.000000	676.000000	676.000000	653.000000	676.000000	676.000000	676.000000	676.000000	676.000000
mean	24.671598	2.202663	0.090237	0.252101	0.161095	0.289098	0.144882	0.411908	12.988299	0.099556	...	0.050296	0.035503	32.871302	21.343195	35.787443	2.806213	0.402367	22.840237	5.698225	2.727115
std	4.585177	2.280752	0.306704	0.283125	0.711026	0.308703	0.269526	0.394940	18.768253	1.328597	...	0.225388	0.185184	31.901418	27.525137	16.484353	3.492556	0.793078	20.625649	6.375467	2.120998
min	15.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.110000	-3.800000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	21.000000	0.000000	0.000000	0.000000	0.000000	0.140000	0.070000	0.250000	2.200000	-0.500000	...	0.000000	0.000000	9.000000	4.000000	26.300000	0.000000	0.000000	6.000000	1.000000	1.847500
50%	24.000000	2.000000	0.000000	0.200000	0.100000	0.250000	0.120000	0.370000	5.500000	0.000000	...	0.000000	0.000000	25.000000	12.000000	36.000000	2.000000	0.000000	18.000000	4.000000	2.480000
75%	28.000000	3.000000	0.000000	0.380000	0.210000	0.370000	0.180000	0.500000	17.600000	0.600000	...	0.000000	0.000000	46.000000	26.000000	44.400000	4.000000	1.000000	34.000000	9.000000	3.315000
max	39.000000	12.000000	2.000000	3.330000	18.000000	4.480000	6.520000	6.520000	165.000000	6.000000	...	2.000000	1.000000	225.000000	251.000000	100.000000	24.000000	5.000000	124.000000	41.000000	36.000000

	age	mp_start	gls_pen	y_cards	r_cards	gls_90	ast_90	ex_gls_90	ex_ast_90	ex_gls_ast_no_pen_90	...	air_tackels_loss	air_tackels_won	air_tackels_won_%	dribble_completed	dribble_to_goal	earned_fouls	fouls_commited	offsides	own_gls	shot_created_90
count	546.000000	546.000000	546.000000	546.000000	546.000000	546.00000	546.000000	546.000000	546.000000	546.000000	...	546.000000	546.000000	521.000000	546.000000	546.000000	546.000000	546.000000	546.000000	546.000000	546.000000
mean	25.230769	15.300366	0.120879	3.683150	0.150183	0.07544	0.076245	0.092326	0.095183	0.182399	...	20.809524	21.172161	46.251440	1.173993	0.122711	22.360806	24.983516	0.893773	0.036630	2.089231
std	4.597351	11.049077	0.638263	3.118511	0.405659	0.12098	0.123187	0.223642	0.258103	0.454040	...	18.383189	23.185246	17.172994	1.954750	0.380194	19.767266	18.727248	1.685251	0.206622	1.817912
min	15.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	22.000000	5.000000	0.000000	1.000000	0.000000	0.00000	0.000000	0.030000	0.020000	0.060000	...	8.000000	6.000000	38.000000	0.000000	0.000000	7.000000	9.000000	0.000000	0.000000	1.182500
50%	25.000000	15.000000	0.000000	3.000000	0.000000	0.03000	0.040000	0.060000	0.060000	0.130000	...	17.000000	15.000000	47.800000	0.000000	0.000000	19.000000	22.000000	0.000000	0.000000	1.860000
75%	29.000000	25.000000	0.000000	5.000000	0.000000	0.11000	0.117500	0.110000	0.110000	0.210000	...	29.000000	29.000000	56.800000	2.000000	0.000000	32.000000	37.000000	1.000000	0.000000	2.670000
max	37.000000	38.000000	7.000000	14.000000	3.000000	1.50000	1.700000	4.700000	4.920000	9.630000	...	157.000000	192.000000	100.000000	22.000000	3.000000	117.000000	91.000000	13.000000	2.000000	25.710000

	age	mp_start	gls_pen	y_cards	r_cards	gls_90	ast_90	ex_gls_90	ex_ast_90	ex_gls_ast_no_pen_90	...	air_tackels_loss	air_tackels_won	air_tackels_won_%	dribble_completed	dribble_to_goal	earned_fouls	fouls_commited	offsides	own_gls	shot_created_90
count	761.000000	761.000000	761.000000	761.000000	761.000000	761.000000	761.000000	761.000000	761.000000	761.000000	...	761.000000	761.000000	742.000000	761.000000	761.000000	761.000000	761.000000	761.000000	761.000000	761.000000
mean	25.459921	16.825230	0.021025	3.378449	0.176084	0.047424	0.066518	0.054442	0.070933	0.124652	...	23.959264	34.052562	55.262938	0.671485	0.064389	14.562418	18.927727	1.162943	0.122208	1.363022
std	4.326027	10.963441	0.265725	2.901226	0.429817	0.097018	0.414848	0.105516	0.282454	0.300206	...	18.268096	30.800032	16.593508	1.304784	0.280612	13.360059	13.524492	2.108190	0.354732	3.554496
min	16.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	22.000000	7.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.020000	0.010000	0.040000	...	9.000000	10.000000	46.825000	0.000000	0.000000	5.000000	8.000000	0.000000	0.000000	0.440000
50%	25.000000	17.000000	0.000000	3.000000	0.000000	0.000000	0.000000	0.040000	0.030000	0.080000	...	21.000000	26.000000	57.100000	0.000000	0.000000	11.000000	17.000000	0.000000	0.000000	0.920000
75%	29.000000	26.000000	0.000000	5.000000	0.000000	0.070000	0.080000	0.060000	0.080000	0.140000	...	36.000000	51.000000	65.650000	1.000000	0.000000	21.000000	28.000000	1.000000	0.000000	1.640000
max	38.000000	38.000000	6.000000	16.000000	3.000000	1.340000	11.250000	2.160000	6.500000	6.500000	...	135.000000	171.000000	100.000000	8.000000	2.000000	91.000000	71.000000	21.000000	2.000000	90.000000

	player_name	nationality	position	team	league	age	birth_year	match_played	match_starts	minutes_played	...	expected_goals	expected_goals_no_pen	expected_assists	expected_goals_and_assists_no_pen	expected_goals_per_90	expected_assists_per_90	expected_goals_and_assists_per_90	expected_goals_no_pen_per_90	expected_goals_and_assists_no_pen_per_90	matches
0	Max Aarons	eng ENG	DF	Norwich City	eng Premier League	21	2000	34	32	2,881	...	0.7	0.7	1.4	2.1	0.02	0.04	0.07	0.02	0.07	Matches
1	Yunis Abdelhamid	ma MAR	DF	Reims	fr Ligue 1	33	1987	34	34	2,983	...	1.2	1.2	0.8	2.1	0.04	0.02	0.06	0.04	0.06	Matches
2	Salis Abdul Samed	gh GHA	MF	Clermont Foot	fr Ligue 1	21	2000	31	29	2,462	...	0.8	0.8	1.1	1.9	0.03	0.04	0.07	0.03	0.07	Matches
3	Laurent Abergel	fr FRA	MF	Lorient	fr Ligue 1	28	1993	34	34	2,956	...	2.0	2.0	2.9	4.9	0.06	0.09	0.15	0.06	0.15	Matches
4	Charles Abi	fr FRA	FW	Saint-Étienne	fr Ligue 1	21	2000	1	1	45	...	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	0.00	Matches
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2916	Martín Zubimendi	es ESP	MF	Real Sociedad	es La Liga	22	1999	36	26	2,591	...	2.0	2.0	1.0	3.0	0.07	0.04	0.10	0.07	0.10	Matches
2917	Szymon Żurkowski	pl POL	MF	Empoli	it Serie A	23	1997	35	29	2,307	...	4.1	4.1	1.5	5.5	0.16	0.06	0.22	0.16	0.22	Matches
2918	Martin Ødegaard	no NOR	MF	Arsenal	eng Premier League	22	1998	36	32	2,785	...	4.8	4.8	6.8	11.6	0.16	0.22	0.38	0.16	0.38	Matches
2919	Milan Đurić	ba BIH	FW	Salernitana	it Serie A	31	1990	33	23	2,165	...	5.8	5.0	3.1	8.1	0.24	0.13	0.37	0.21	0.34	Matches
2920	Filip Đuričić	rs SRB	MF,FW	Sassuolo	it Serie A	29	1992	12	9	671	...	2.0	2.0	1.3	3.3	0.27	0.17	0.44	0.27	0.44	Matches

	first_name	last_name	full_name	team	position	value
0	Max	Aarons	Max Aarons	Norwich City	DF	NaN
1	Yunis	Abdelhamid	Yunis Abdelhamid	Reims	DF	NaN
2	Salis	Abdul Samed	Salis Abdul Samed	Clermont Foot	MF	NaN
3	Laurent	Abergel	Laurent Abergel	Lorient	MF	NaN
4	Charles	Abi	Charles Abi	Saint-Étienne	FW	NaN
...	...	...	...	...	...	...
2916	Martín	Zubimendi	Martín Zubimendi	Real Sociedad	MF	NaN
2917	Szymon	Żurkowski	Szymon Żurkowski	Empoli	MF	NaN
2918	Martin	Ødegaard	Martin Ødegaard	Arsenal	MF	NaN
2919	Milan	Đurić	Milan Đurić	Salernitana	FW	NaN
2920	Filip	Đuričić	Filip Đuričić	Sassuolo	MF,FW	NaN

	player_name	age	position	team	value
0	Max Aarons	21	DF	Norwich City	$24.20m
1	Yunis Abdelhamid	33	DF	Reims	$1.32m
2	Salis Abdul Samed	21	MF	Clermont Foot	$3.30m
3	Laurent Abergel	28	MF	Lorient	$3.30m
4	Charles Abi	21	FW	Saint-Étienne	NaN
...	...	...	...	...	...
2916	Martín Zubimendi	22	MF	Real Sociedad	$33.00m
2917	Szymon Żurkowski	23	MF	Empoli	$7.70m
2918	Martin Ødegaard	22	MF	Arsenal	$49.50m
2919	Milan Đurić	31	FW	Salernitana	NaN
2920	Filip Đuričić	29	MF,FW	Sassuolo	NaN

	name	nation	pos	team	league	age	born	mp	mp_start	minutes	...	ex_gls_no_pen	ex_ast	ex_gls_ast_no_pen	ex_gls_90	ex_ast_90	ex_gls_ast_90	ex_gls_no_pen_90	ex_gls_ast_no_pen_90	matches	value
1036	Nicolás González	ar ARG	FW	Fiorentina	it Serie A	23	1998	33	26	2,357	...	7.1	3.8	10.9	0.36	0.15	0.50	0.27	0.42	Matches	$27.50m
1355	Moise Kean	it ITA	FW	Juventus	it Serie A	21	2000	32	12	1,098	...	4.1	0.1	4.2	0.34	0.01	0.35	0.34	0.35	Matches	$26.40m
2067	João Pedro	it ITA	FW	Cagliari	it Serie A	29	1992	37	37	3,323	...	9.9	3.1	13.0	0.35	0.08	0.43	0.27	0.35	Matches	$13.20m

	name	nation	pos	team	league	age	born	mp	mp_start	minutes	...	ex_gls_no_pen	ex_ast	ex_gls_ast_no_pen	ex_gls_90	ex_ast_90	ex_gls_ast_90	ex_gls_no_pen_90	ex_gls_ast_no_pen_90	matches	value
2066	João Pedro	br BRA	FW,MF	Watford	eng Premier League	19	2001	28	15	1,644	...	4.2	2.6	6.8	0.23	0.14	0.37	0.23	0.37	Matches	$15.40m
2067	João Pedro	it ITA	FW	Cagliari	it Serie A	29	1992	37	37	3,323	...	9.9	3.1	13.0	0.35	0.08	0.43	0.27	0.35	Matches	$13.20m

	name	mp	league	gls	ast	value
0	Erling Haaland	24.0	de Bundesliga	22.0	8.0	165.000
1	Vinicius Júnior	35.0	es La Liga	17.0	10.0	110.000
2	Phil Foden	28.0	eng Premier League	9.0	5.0	99.000
3	Harry Kane	37.0	eng Premier League	17.0	9.0	99.000
4	Mohamed Salah	35.0	eng Premier League	23.0	13.0	99.000
...	...	...	...	...	...	...
2168	Stephan Fürstner	1.0	de Bundesliga	0.0	0.0	0.083
2169	Ebenezer Annan	1.0	it Serie A	0.0	0.0	0.083
2170	Alessandro Berardi	1.0	it Serie A	0.0	0.0	0.055
2171	Davide Marfella	1.0	it Serie A	0.0	0.0	0.055
2172	Andrei Motoc	1.0	it Serie A	0.0	0.0	0.028

	name	nation	pos	team	league	age	mp	mp_start	gls	ast	...	air_tackels_won_%	born	dribble	dribble_completed	dribble_to_goal	earned_fouls	fouls_commited	offsides	own_gls	shot_created_90
0	Erling Haaland	no NOR	ATT	Dortmund	de Bundesliga	21.0	24.0	21.0	22.0	8.0	...	54.9	2000.0	8.0	4.0	2.0	17.0	17.0	14.0	0.0	2.59
1	Vinicius Júnior	br BRA	ATT	Real Madrid	es La Liga	21.0	35.0	30.0	17.0	10.0	...	17.4	2000.0	59.0	21.0	3.0	82.0	42.0	26.0	0.0	5.69
2	Phil Foden	eng ENG	ATT	Manchester City	eng Premier League	21.0	28.0	24.0	9.0	5.0	...	19.5	2000.0	59.0	3.0	0.0	38.0	17.0	13.0	0.0	3.51
3	Harry Kane	eng ENG	ATT	Tottenham	eng Premier League	28.0	37.0	36.0	17.0	9.0	...	47.3	1993.0	40.0	10.0	1.0	70.0	46.0	17.0	0.0	3.26
4	Mohamed Salah	eg EGY	ATT	Liverpool	eng Premier League	29.0	35.0	30.0	23.0	13.0	...	22.0	1992.0	51.0	21.0	5.0	30.0	13.0	12.0	0.0	4.33
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2168	Stephan Fürstner	de GER	MID	Mainz 05	de Bundesliga	33.0	1.0	0.0	0.0	0.0	...	NaN	1987.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00
2169	Ebenezer Annan	gh GHA	DEF	Bologna	it Serie A	18.0	1.0	0.0	0.0	0.0	...	NaN	2002.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00
2170	Alessandro Berardi	it ITA	GK	Hellas Verona	it Serie A	30.0	1.0	1.0	0.0	0.0	...	NaN	1991.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00
2171	Davide Marfella	it ITA	GK	Napoli	it Serie A	21.0	1.0	0.0	0.0	0.0	...	NaN	1999.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00
2172	Andrei Motoc	ro ROU	DEF	Salernitana	it Serie A	19.0	1.0	1.0	0.0	0.0	...	0.0	2002.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.00

	name	age	pos	team	value
0	Kevin De Bruyne	30.0	MID	Manchester City	93.5
1	Joshua Kimmich	26.0	MID	Bayern Munich	88.0
2	Declan Rice	22.0	MID	West Ham	88.0
3	Jude Bellingham	18.0	MID	Dortmund	88.0
4	Sergej Milinković-Savić	26.0	MID	Lazio	77.0

	name	age	pos	team	value
0	Trent Alexander-Arnold	22.0	DEF	Liverpool	88.0
1	Rúben Dias	24.0	DEF	Manchester City	82.5
2	Matthijs de Ligt	21.0	DEF	Juventus	77.0
3	Alphonso Davies	20.0	DEF	Bayern Munich	77.0
4	João Cancelo	27.0	DEF	Manchester City	71.5

		value
team	league
Manchester City	eng Premier League	42.156190
Chelsea	eng Premier League	35.893478
Liverpool	eng Premier League	34.342000
Bayern Munich	de Bundesliga	32.153846
Atlético Madrid	es La Liga	31.900000

	value	predicted_value
0	3.85	5.389112
1	1.54	2.027912
2	8.80	18.468805
3	77.00	49.620604
4	1.32	7.963421
...	...	...
149	9.90	7.102033
150	2.20	-4.890108
151	2.20	12.189019
152	27.50	29.479843
153	2.75	8.987917

	value	predicted_value
0	16.50	11.353084
1	7.70	27.738675
2	27.50	18.343369
3	6.60	19.924336
4	0.99	-16.057368
...	...	...
92	2.42	13.663541
93	1.10	0.295520
94	16.50	10.363969
95	19.80	-2.023898
96	1.65	5.853899

	value	predicted_value
0	3.30	16.028467
1	1.10	14.013604
2	19.80	18.974339
3	1.98	-5.516923
4	2.75	-5.214824
...	...	...
196	22.00	11.173663
197	22.00	21.697696
198	13.20	5.573360
199	3.30	1.895253
200	1.43	-0.931125

	value
team
Manchester City	885.28
Liverpool	858.55
Bayern Munich	836.00
Chelsea	825.55
Real Madrid	691.02

Import All Required Modules¶

Web Scaraping¶

List Of Statistics URL from Seasson 2021-2022¶

Function to check url status (OK / ERROR)¶

Check the status of each url from the list¶

Build Soup for each url category¶

Create DataFrames¶

Function to Export & Load CSV¶

Function for Split Full Name to First and Last¶

* Require for searching the players vlaue on transfermarket¶

Function to check player url status on transfermarket¶

Function to search url page on transfermarket¶

Function to get player value from transfermarket¶

Append value column from df_value to each dataframe¶

Clean Data¶

Check duplicated value¶

Replace "Th." in value column with K¶

Function to define player position¶

Function for merge DataFrame¶

Function for define player position 2¶

Function to change value rows from K to M¶

Create New DataFrame by Player Position¶

Exploratory Data Analyis¶

Top 10 valuable players¶

Top 5 valuable ATT players¶

Top 5 valuable MID players¶

Top 5 valuable DEF players¶

Mean players value based on position¶

Top 5 Teams by player value (mean)¶

Top 5 Teams by total players value¶

Players distribution by age¶

Players distribution by age and value¶

Players distribution by position¶

Players value by age¶

Dataframes (ATT, MID, DEF) Describe¶

Heatmaps¶

ATT Dataframe Heatmap¶

MID Dataframe Heatmap¶

DEF Dataframe Heatmap¶

Correlation¶

ATT Correlation¶

MID Correlation¶

DEF Correlation¶

ATT Value Prediction¶

MID Value Prediction¶

DEF Value Prediction¶